@proceedings {7044,
	title = {Behavior-Driven Synthesis of Human Dynamics},
	year = {2021},
	url = {https://arxiv.org/abs/2103.04677},
	author = {Andreas Blattmann and Timo Milbich and Michael Dorkenwald and Bj{\"o}rn Ommer}
}
@article {7071,
	title = {Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning},
	year = {2021},
	url = {https://arxiv.org/abs/2107.09562},
	author = {Timo Milbich and Karsten Roth and Samarth Sinha and Ludwig Schmidt and Marzyeh Ghassemi and Bj{\"o}rn Ommer}
}
@article {7073,
	title = {Improving Deep Metric Learning by Divide and Conquer},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
	year = {2021},
	abstract = {Deep metric learning (DML) is a cornerstone of many computer vision applications. It aims at learning a mapping from the input domain to an embedding space, where semantically similar objects are located nearby and dissimilar objects far from another. The target similarity on the training data is defined by user in form of ground-truth class labels. However, while the embedding space learns to mimic the user-provided similarity on the training data, it should also generalize to novel categories not seen during training. Besides user-provided groundtruth training labels, a lot of additional visual factors (such as viewpoint changes or shape peculiarities) exist and imply different notions of similarity between objects, affecting the generalization on the images unseen during training. However, existing approaches usually directly learn a single embedding space on all available training data, struggling to encode all different types of relationships, and do not generalize well. We propose to build a more expressive representation by jointly splitting the embedding space and the data hierarchically into smaller sub-parts. We successively focus on smaller subsets of the training data, reducing its variance and learning a different embedding subspace for each data subset. Moreover, the subspaces are learned jointly to cover not only the intricacies, but the breadth of the data as well. Only after that, we build the final embedding from the subspaces in the conquering stage. The proposed algorithm acts as a transparent wrapper that can be placed around arbitrary existing DML methods. Our approach significantly improves upon the state-of-the-art on image retrieval, clustering, and re-identification tasks evaluated using CUB200-2011, CARS196, Stanford Online Products, In-shop Clothes, and PKU VehicleID datasets.},
	url = {https://arxiv.org/abs/2109.04003},
	author = {Sanakoyeu, Artsiom and Pingchuan Ma and Tschernezki, V. and Bj{\"o}rn Ommer}
}
@article {7086,
	title = {Integration of Cell Growth and Asymmetric Division during Lateral Root Initiation in Arabidopsis thaliana},
	journal = {Plant and Cell Physiology},
	volume = {62},
	year = {2021},
	pages = {1269-1279},
	doi = {10.1093/pcp/pcab038},
	author = {Sch{\"u}tz, LM and Louveaux, M and Vilches-Barro, A and Bouziri, S and Cerrone, L and Wolny, A and Kreshuk, A and Hamprecht, FA and Maizel, A}
}
@conference {7068,
	title = {iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis},
	booktitle = {Proceedings of the International Conference on Computer Vision (ICCV)},
	year = {2021},
	url = {https://arxiv.org/abs/2107.02790},
	author = {Andreas Blattmann and Timo Milbich and Michael Dorkenwald and Bj{\"o}rn Ommer}
}
@article {7022,
	title = {Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera},
	journal = {BioEssays},
	volume = {43},
	year = {2021},
	doi = {10.1002/bies.202000257},
	author = {Pape, C and Remme, R and Wolny, A and Olberg, S and Wolf, S and Cerrone, L and Cortese, M and Klaus, S and Lucic, B and Ullrich, S and Anders-{\"O}sswein, M and Wolf, S and Cerikan, B and Neufeldt, C J and Ganter, M and Schnitzler, P and Merle, U and Lusic, M and Boulant, S and Stanifer, M and Bartenschlager, R and Hamprecht, F A and Kreshuk, A and Tischer, C and Kr{\"a}usslich, H.-G. and M{\"u}ller, B and Laketa, V}
}
@proceedings {7051,
	title = {S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning},
	year = {2021},
	url = {https://arxiv.org/abs/2009.08348},
	author = {Karsten Roth and Timo Milbich and Bj{\"o}rn Ommer and Joseph Paul Cohen and Marzyeh Ghassemi}
}
@proceedings {7053,
	title = {Stochastic Image-to-Video Synthesis usin cINNs},
	year = {2021},
	author = {Michael Dorkenwald and Timo Milbich and Andreas Blattmann and Robin Rombach and Konstantinos G. Derpanis and Bj{\"o}rn Ommer}
}
@proceedings {7063,
	title = {Understanding Object Dynamics for Interactive Image-to-Video Synthesis},
	year = {2021},
	abstract = {What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level. Training requires only videos of moving objects but no information of the underlying manipulation of the physical scene. Our generative model learns to infer natural object dynamics as a response to user interaction and learns about the interrelations between different object body regions. Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time. In contrast to existing work on video prediction, we do not synthesize arbitrary realistic videos but enable local interactive control of the deformation. Our model is not restricted to particular object categories and can transfer dynamics onto novel unseen object instances. Extensive experiments on diverse objects demonstrate the effectiveness of our approach compared to common video prediction frameworks.},
	url = {https://arxiv.org/abs/2106.11303v1},
	author = {Andreas Blattmann and Timo Milbich and Michael Dorkenwald and Bj{\"o}rn Ommer}
}
@article {7090,
	title = {Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution},
	journal = {eLife},
	volume = {9},
	year = {2020},
	doi = {10.7554/eLife.57613},
	author = {Wolny, A and Cerrone, L and Vijayan, A and Tofanelli, R and Vilches-Barro, A and Louveaux, M and Wenzel, C and Strauss, S and Wilson-Sanchez, D and Lymbouridou, R and Steigleder, SS and Pape, C and Bailoni, A and Duran-Nebreda, S and Bassel, GW and Lohmann, JU and Tsiantis, M and Hamprecht, FA and Schneitz, K and Maizel, A and Kreshuk, A}
}
@conference {Radev2020,
	title = {BayesFlow: Learning complex stochastic models with invertible neural networks},
	year = {2020},
	abstract = {Estimating the parameters of mathematical models is a common problem in almost all branches of science. However, this problem can prove notably difficult when processes and model descriptions become increasingly complex and an explicit likelihood function is not available. With this work, we propose a novel method for globally amortized Bayesian inference based on invertible neural networks which we call BayesFlow. The method uses simulation to learn a global estimator for the probabilistic mapping from observed data to underlying model parameters. A neural network pre-trained in this way can then, without additional training or optimization, infer full posteriors on arbitrary many real data sets involving the same model family. In addition, our method incorporates a summary network trained to embed the observed data into maximally informative summary statistics. Learning summary statistics from data makes the method applicable to modeling scenarios where standard inference techniques with hand-crafted summary statistics fail. We demonstrate the utility of BayesFlow on challenging intractable models from population dynamics, epidemiology, cognitive science and ecology. We argue that BayesFlow provides a general framework for building reusable Bayesian parameter estimation machines for any process model from which data can be simulated.},
	url = {http://arxiv.org/abs/2003.06281},
	author = {Radev, Stefan T. and Mertens, Ulf K. and Voss, Andreass and Lynton Ardizzone and K{\"o}the, Ullrich}
}
@article {7029,
	title = {Deep learning of cuneiform sign detection with weak supervision using transliteration alignment},
	journal = {PLoS ONE},
	volume = {15},
	year = {2020},
	month = {12/2020},
	type = {Journal},
	chapter = {1-21},
	abstract = {The cuneiform script provides a glimpse into our ancient history. However, reading age-old clay tablets is time-consuming and requires years of training. To simplify this process, we propose a deep-learning based sign detector that locates and classifies cuneiform signs in images of clay tablets. Deep learning requires large amounts of training data in the form of bounding boxes around cuneiform signs, which are not readily available and costly to obtain in the case of cuneiform script. To tackle this problem, we make use of existing transliterations, a sign-by-sign representation of the tablet content in Latin script. Since these do not provide sign localization, we propose a weakly supervised approach: We align tablet images with their corresponding transliterations to localize the transliterated signs in the tablet image, before using these localized signs in place of annotations to re-train the sign detector. A better sign detector in turn boosts the quality of the alignments. We combine these steps in an iterative process that enables training a cuneiform sign detector from transliterations only. While our method works weakly supervised, a small number of annotations further boost the performance of the cuneiform sign detector which we evaluate on a large collection of clay tablets from the Neo-Assyrian period. To enable experts to directly apply the sign detector in their study of cuneiform texts, we additionally provide a web application for the analysis of clay tablets with a trained cuneiform sign detector.},
	keywords = {cuneiform script, deep learning, sign detection, Weakly supervised learning},
	doi = {https://doi.org/10.1371/journal.pone.0243039},
	url = {https://hci.iwr.uni-heidelberg.de/compvis/projects/cuneiform},
	author = {Tobias Dencker and Pablo Klinkisch and Stefan M Maul and Bj{\"o}rn Ommer}
}
@proceedings {6934,
	title = {DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning},
	year = {2020},
	url = {https://arxiv.org/abs/2004.13458},
	author = {Timo Milbich and Karsten Roth and Homanga Bharadhwaj and Samarth Sinha and Yoshua Bengio and Bj{\"o}rn Ommer and Joseph Paul Cohen}
}
@article {Ardizzone2020,
	title = {Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling},
	year = {2020},
	month = {jan},
	abstract = {Generative models are more informative about underlying phenomena than discriminative ones and offer superior uncertainty quantification and out-of-distribution robustness. However, these advantages often come at the expense of reduced classification accuracy. The Information Bottleneck objective (IB) formulates this trade-off in a clean information-theoretic way, but its practical application is hampered by a lack of accurate high-dimensional estimators of mutual information (MI), its main constituent. To overcome this limitation, we develop the theory and methodology of IB-INNs, which optimize the IB objective by means of Invertible Neural Networks (INNs), without the need for approximations of MI. Our experiments show that IB-INNs allow for a precise adjustment of the generative/discriminative trade-off: They learn accurate models of the class conditional likelihoods, generalize well to unseen data and reliably detect out-of-distribution examples, while at the same time exhibiting classification accuracy close to purely discriminative feed-forward networks.},
	url = {http://arxiv.org/abs/2001.06448},
	author = {Lynton Ardizzone and Mackowiak, Radek and Carsten Rother and Ullrich K{\"o}the}
}
@conference {6386,
	title = {PADS: Policy-Adapted Sampling for Visual Similarity Learning},
	booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	volume = {1},
	year = {2020},
	chapter = {1},
	url = {https://arxiv.org/abs/2003.11113},
	author = {Timo Milbich and Karsten Roth and Bj{\"o}rn Ommer}
}
@proceedings {6390,
	title = {Revisiting Training Strategies and Generalization Performance in Deep Metric Learning},
	year = {2020},
	url = {https://arxiv.org/pdf/2002.08473.pdf},
	author = {Karsten Roth and Timo Milbich and Samarth Sinha and Prateek Gupta and Bj{\"o}rn Ommer and Joseph Paul Cohen}
}
@conference {Cvpr2020,
	title = {Self-Supervised Viewpoint Learning From Image Collections},
	booktitle = {CONSAC},
	year = {2020},
	abstract = {Training deep neural networks to estimate the viewpoint of objects requires large labeled training datasets. However, manually labeling viewpoints is notoriously hard, error-prone, and time-consuming. On the other hand, it is relatively easy to mine many unlabelled images of an object category from the internet, e.g., of cars or faces. We seek to answer the research question of whether such un-labeled collections of in-the-wild images can be successfully utilized to train viewpoint estimation networks for general object categories purely via self-supervision. Self-supervision here refers to the fact that the only true supervisory signal that the network has is the input image itself. We propose a novel learning framework which incorporates an analysis-by-synthesis paradigm to reconstruct images in a viewpoint aware manner with a generative network, along with symmetry and adversarial constraints to successfully supervise our viewpoint estimation network. We show that our approach performs competitively to fully-supervised approaches for several object categories like human faces, cars, buses, and trains. Our work opens up further research in self-supervised viewpoint learning and serves as a robust baseline for it. We open-source our code at https://github.com/NVlabs/SSV .},
	url = {https://github.com/NVlabs/SSV},
	author = {S.K. Mustikovela and V. Jampani and S. De Mello and S. Liu and U. Iqbal and C. Rother and J. Kautz}
}
@article {6389,
	title = {Sharing Matters for Generalization in Deep Metric Learning},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
	year = {2020},
	doi = {10.1109/TPAMI.2020.3009620},
	url = {https://arxiv.org/abs/2004.05582},
	author = {Timo Milbich and Karsten Roth and Biagio Brattoli and Bj{\"o}rn Ommer}
}
@article {6339,
	title = {Unsupervised Representation Learning by Discovering Reliable Image Relations},
	journal = {Pattern Recognition},
	volume = {102},
	year = {2020},
	month = {June 2020},
	url = {http://arxiv.org/abs/1911.07808},
	author = {Timo Milbich and Omair Ghori and Bj{\"o}rn Ommer}
}
@incollection {jaehne2020,
	title = {What controls air-sea gas exchange at extreme wind speeds? Evidence from laboratory experiments},
	year = {2020},
	pages = {133{\textendash}150},
	publisher = {Springer},
	doi = {10.1007/978-3-030-36371-0_10},
	author = {Bernd J{\"a}hne},
	editor = {P. Vlahos and E. Monahan}
}
@article {Kleesiek2019,
	title = {Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study},
	journal = {Investigative Radiology},
	volume = {54},
	year = {2019},
	pages = {653{\textendash}660},
	abstract = {Objectives Gadolinium-based contrast agents (GBCAs) have become an integral part in daily clinical decision making in the last 3 decades. However, there is a broad consensus that GBCAs should be exclusively used if no contrast-free magnetic resonance imaging (MRI) technique is available to reduce the amount of applied GBCAs in patients. In the current study, we investigate the possibility of predicting contrast enhancement from noncontrast multiparametric brain MRI scans using a deep-learning (DL) architecture. Materials and Methods A Bayesian DL architecture for the prediction of virtual contrast enhancement was developed using 10-channel multiparametric MRI data acquired before GBCA application. The model was quantitatively and qualitatively evaluated on 116 data sets from glioma patients and healthy subjects by comparing the virtual contrast enhancement maps to the ground truth contrast-enhanced T1-weighted imaging. Subjects were split in 3 different groups: Enhancing tumors (n = 47), nonenhancing tumors (n = 39), and patients without pathologic changes (n = 30). The tumor regions were segmented for a detailed analysis of subregions. The influence of the different MRI sequences was determined. Results Quantitative results of the virtual contrast enhancement yielded a sensitivity of 91.8\% and a specificity of 91.2\%. T2-weighted imaging, followed by diffusion-weighted imaging, was the most influential sequence for the prediction of virtual contrast enhancement. Analysis of the whole brain showed a mean area under the curve of 0.969 {\textpm} 0.019, a peak signal-to-noise ratio of 22.967 {\textpm} 1.162 dB, and a structural similarity index of 0.872 {\textpm} 0.031. Enhancing and nonenhancing tumor subregions performed worse (except for the peak signal-to-noise ratio of the nonenhancing tumors). The qualitative evaluation by 2 raters using a 4-point Likert scale showed good to excellent (3-4) results for 91.5\% of the enhancing and 92.3\% of the nonenhancing gliomas. However, despite the good scores and ratings, there were visual deviations between the virtual contrast maps and the ground truth, including a more blurry, less nodular-like ring enhancement, few low-contrast false-positive enhancements of nonenhancing gliomas, and a tendency to omit smaller vessels. These "features" were also exploited by 2 trained radiologists when performing a Turing test, allowing them to discriminate between real and virtual contrast-enhanced images in 80\% and 90\% of the cases, respectively. Conclusions The introduced model for virtual gadolinium enhancement demonstrates a very good quantitative and qualitative performance. Future systematic studies in larger patient collectives with varying neurological disorders need to evaluate if the introduced virtual contrast enhancement might reduce GBCA exposure in clinical practice.},
	keywords = {Bayesian uncertainty, deep learning, gadolinium-based contrast agents, glioma, multiparametric MRI},
	issn = {15360210},
	doi = {10.1097/RLI.0000000000000583},
	author = {Kleesiek, Jens and Morshuis, Jan Nikolas and Isensee, Fabian and Deike-Hofmann, Katerina and Paech, Daniel and Kickingereder, Philipp and K{\"o}the, Ullrich and Rother, Carsten and Forsting, Michael and Wick, Wolfgang and Bendszus, Martin and Schlemmer, Heinz Peter and Radbruch, Alexander}
}
@conference {Mackowiak2019,
	title = {CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation},
	booktitle = {British Machine Vision Conference 2018, BMVC 2018},
	year = {2019},
	abstract = {State of the art methods for semantic image segmentation are trained in a supervised fashion using a large corpus of fully labeled training images. However, gathering such a corpus is expensive, due to human annotation effort, in contrast to gathering unlabeled data. We propose an active learning-based strategy, called CEREALS, in which a human only has to hand-label a few, automatically selected, regions within an unlabeled image corpus. This minimizes human annotation effort while maximizing the performance of a semantic image segmentation method. The automatic selection procedure is achieved by: a) using a suitable information measure combined with an estimate about human annotation effort, which is inferred from a learned cost model, and b) exploiting the spatial coherency of an image. The performance of CEREALS is demonstrated on Cityscapes, where we are able to reduce the annotation effort to 17\%, while keeping 95\% of the mean Intersection over Union (mIoU) of a model that was trained with the fully annotated training set of Cityscapes.},
	author = {Mackowiak, Radek and Lenz, Philip and Ghori, Omair and Ferran Diego and Lange, Oliver and Carsten Rother}
}
@article {7083,
	title = {Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures},
	journal = {Nature Communications},
	volume = {13;10(1)},
	year = {2019},
	doi = {10.1038/s41467-019-09879-3},
	author = {Imle, A and Kumberger, P and Schnellb{\"a}cher, ND and Fehr, J and Carillo-Bustamente, P and Ales, J and Schmidt, P and Ritter, C and Godinez, WJ and M{\"u}ller, B and Rohr, K and Hamprecht, FA and Schwarz, US and Graw, F and Fackler, OT}
}
@conference {AbuAlhaija2019,
	title = {Geometric Image Synthesis},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11366 LNCS},
	year = {2019},
	pages = {85{\textendash}100},
	abstract = {The task of generating natural images from 3D scenes has been a long standing goal in computer graphics. On the other hand, recent developments in deep neural networks allow for trainable models that can produce natural-looking images with little or no knowledge about the scene structure. While the generated images often consist of realistic looking local patterns, the overall structure of the generated images is often inconsistent. In this work we propose a trainable, geometry-aware image generation method that leverages various types of scene information, including geometry and segmentation, to create realistic looking natural images that match the desired scene structure. Our geometrically-consistent image synthesis method is a deep neural network, called Geometry to Image Synthesis (GIS) framework, which retains the advantages of a trainable method, e.g., differentiability and adaptiveness, but, at the same time, makes a step towards the generalizability, control and quality output of modern graphics rendering engines. We utilize the GIS framework to insert vehicles in outdoor driving scenes, as well as to generate novel views of objects from the Linemod dataset. We qualitatively show that our network is able to generalize beyond the training set to novel scene geometries, object shapes and segmentations. Furthermore, we quantitatively show that the GIS framework can be used to synthesize large amounts of training data which proves beneficial for training instance segmentation models.},
	isbn = {9783030208752},
	issn = {16113349},
	doi = {10.1007/978-3-030-20876-9_6},
	url = {https://youtu.be/W2tFCz9xJoU},
	author = {Hassan Abu Alhaija and Mustikovela, Siva Karthik and Geiger, Andreas and Carsten Rother}
}
@conference {HosseiniJafari2019,
	title = {iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11363 LNCS},
	year = {2019},
	pages = {477{\textendash}492},
	abstract = {We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded. Recent RGB-D-based methods are robust to moderate degrees of occlusion. For RGB inputs, no previous method works well for partly occluded objects. Our main contribution is to present the first deep learning-based system that estimates accurate poses for partly occluded objects from RGB-D and RGB input. We achieve this with a new instance-aware pipeline that decomposes 6D object pose estimation into a sequence of simpler steps, where each step removes specific aspects of the problem. The first step localizes all known objects in the image using an instance segmentation network, and hence eliminates surrounding clutter and occluders. The second step densely maps pixels to 3D object surface positions, so called object coordinates, using an encoder-decoder network, and hence eliminates object appearance. The third, and final, step predicts the 6D pose using geometric optimization. We demonstrate that we significantly outperform the state-of-the-art for pose estimation of partly occluded objects for both RGB and RGB-D input.},
	isbn = {9783030208929},
	issn = {16113349},
	doi = {10.1007/978-3-030-20893-6_30},
	author = {Omid Hosseini Jafari and Mustikovela, Siva Karthik and Pertsch, Karl and Brachmann, Eric and Carsten Rother}
}
@conference {Leistner2019,
	title = {Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift},
	booktitle = {Proceedings - 2019 International Conference on 3D Vision, 3DV 2019},
	year = {2019},
	month = {sep},
	pages = {249{\textendash}257},
	abstract = {We propose a method for depth estimation from light field data, based on a fully convolutional neural network architecture. Our goal is to design a pipeline which achieves highly accurate results for small-and wide-baseline light fields. Since light field training data is scarce, all learning-based approaches use a small receptive field and operate on small disparity ranges. In order to work with wide-baseline light fields, we introduce the idea of EPI-Shift: To virtually shift the light field stack which enables to retain a small receptive field, independent of the disparity range. In this way, our approach {\textquoteright}learns to think outside the box of the receptive field". Our network performs joint classification of integer disparities and regression of disparity-offsets. A U-Net component provides excellent long-range smoothing. EPI-Shift considerably outperforms the state-of-the-art learning-based approaches and is on par with hand-crafted methods. We demonstrate this on a publicly available, synthetic, small-baseline benchmark and on large-baseline real-world recordings.},
	keywords = {Computer vision, deep learning, depth estimation, light fields, Stereo},
	isbn = {9781728131313},
	doi = {10.1109/3DV.2019.00036},
	url = {http://arxiv.org/abs/1909.09059 http://dx.doi.org/10.1109/3DV.2019.00036},
	author = {Titus Leistner and Schilling, Hendrik and Mackowiak, Radek and Gumhold, Stefan and Carsten Rother}
}
@conference {Adler2019,
	title = {Out of Distribution Detection for Intra-operative Functional Imaging},
	booktitle = {MICCAI UNSURE Workshop 2019},
	volume = {11840 LNCS},
	year = {2019},
	pages = {75{\textendash}82},
	abstract = {Multispectral optical imaging is becoming a key tool in the operating room. Recent research has shown that machine learning algorithms can be used to convert pixel-wise reflectance measurements to tissue parameters, such as oxygenation. However, the accuracy of these algorithms can only be guaranteed if the spectra acquired during surgery match the ones seen during training. It is therefore of great interest to detect so-called out of distribution (OoD) spectra to prevent the algorithm from presenting spurious results. In this paper we present an information theory based approach to OoD detection based on the widely applicable information criterion (WAIC). Our work builds upon recent methodology related to invertible neural networks (INN). Specifically, we make use of an ensemble of INNs as we need their tractable Jacobians in order to compute the WAIC. Comprehensive experiments with in silico, and in vivo multispectral imaging data indicate that our approach is well-suited for OoD detection. Our method could thus be an important step towards reliable functional imaging in the operating room.},
	issn = {16113349},
	doi = {10.1007/978-3-030-32689-0_8},
	author = {Adler, Tim J and Ayala, Leonardo and Lynton Ardizzone and Kenngott, Hannes G and Vemuri, Anant and M{\"u}ller-Stich, Beat P and Carsten Rother and Ullrich K{\"o}the and Maier-Hein, Lena}
}
@conference {6301,
	title = {Unsupervised Part-Based Disentangling of Object Shape and Appearance},
	booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Oral + Best paper finalist: top 45 / 5160 submissions)},
	year = {2019},
	author = {Dominik Lorenz and Leonard Bereska and Timo Milbich and Bj{\"o}rn Ommer}
}
@proceedings {6300,
	title = {Using a Transformation Content Block For Image Style Transfer},
	year = {2019},
	author = {Dmytro Kotovenko and Sanakoyeu, A. and Sabine Lang and Ma, P. and Bj{\"o}rn Ommer}
}
@article {Alhaija2018,
	title = {Augmented Reality Meets Computer Vision},
	journal = {International Journal of Computer Vision},
	volume = {In press},
	year = {2018},
	pages = {1{\textendash}13},
	abstract = {The success of deep learning in computer vision is based on availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation and object detection models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment real images with virtual objects. This allows us to create realistic composite images which exhibit both realistic background appearance and a large number of complex object arrangements. In contrast to modeling complete 3D environments, our augmentation approach requires only a few user interactions in combination with 3D shapes of the target object. Through extensive experimentation, we conclude the right set of parameters to produce augmented data which can maximally enhance the performance of instance segmentation models. Further, we demonstrate the utility of our approach on training standard deep models for semantic instance segmentation and object detection of cars in outdoor driving scenes. We test the models trained on our augmented data on the KITTI 2015 dataset, which we have annotated with pixel-accurate ground truth, and on Cityscapes dataset. Our experiments demonstrate that models trained on augmented imagery generalize better than those trained on synthetic data or models trained on limited amount of annotated real data.},
	keywords = {autonomous driving, data augmenta-, instance segmentation, synthetic training data, tion},
	author = {Hassan Abu Alhaija and Mustikovela, Siva Karthik and Mescheder, Lars and Geiger, Andreas and Carsten Rother}
}
@article {6268,
	title = {Augmented Reality Meets Computer Vision Efficient Data Generation for Urban Driving Scenes},
	journal = {IJCV},
	year = {2018},
	pages = {1-12},
	doi = {10.1007/s11263-018-1070-x},
	author = {Hassan Abu Alhaija and Mustikovela, S K and Mescheder, A and Geiger, C and Carsten Rother}
}
@article {AbuAlhaija2018,
	title = {Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes},
	journal = {International Journal of Computer Vision},
	volume = {126},
	number = {9},
	year = {2018},
	month = {aug},
	pages = {961{\textendash}972},
	abstract = {The success of deep learning in computer vision is based on the availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Unfortunately, creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation and object detection models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment these images with virtual objects. In contrast to modeling complete 3D environments, our data augmentation approach requires only a few user interactions in combination with 3D models of the target object category. Leveraging our approach, we introduce a novel dataset of augmented urban driving scenes with 360 degree images that are used as environment maps to create realistic lighting and reflections on rendered objects. We analyze the significance of realistic object placement by comparing manual placement by humans to automatic methods based on semantic scene analysis. This allows us to create composite images which exhibit both realistic background appearance as well as a large number of complex object arrangements. Through an extensive set of experiments, we conclude the right set of parameters to produce augmented data which can maximally enhance the performance of instance segmentation models. Further, we demonstrate the utility of the proposed approach on training standard deep models for semantic instance segmentation and object detection of cars in outdoor driving scenarios. We test the models trained on our augmented data on the KITTI 2015 dataset, which we have annotated with pixel-accurate ground truth, and on the Cityscapes dataset. Our experiments demonstrate that the models trained on augmented imagery generalize better than those trained on fully synthetic data or models trained on limited amounts of annotated real data.},
	keywords = {autonomous driving, Data augmentation, instance segmentation, Object detection, synthetic training data},
	issn = {15731405},
	doi = {10.1007/s11263-018-1070-x},
	url = {http://arxiv.org/abs/1708.01566},
	author = {Hassan Abu Alhaija and Mustikovela, Siva Karthik and Mescheder, Lars and Geiger, Andreas and Carsten Rother}
}
@conference {Hodan2018,
	title = {BOP: Benchmark for 6D object pose estimation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11214 LNCS},
	year = {2018},
	month = {aug},
	pages = {19{\textendash}35},
	abstract = {We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: (i) eight datasets in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, (ii) an evaluation methodology with a pose-error function that deals with pose ambiguities, (iii) a comprehensive evaluation of 15 diverse recent methods that captures the status quo of the field, and (iv) an online evaluation system that is open for continuous submission of new results. The evaluation shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methods based on 3D local features. The project website is available at bop.felk.cvut.cz.},
	isbn = {9783030012489},
	issn = {16113349},
	doi = {10.1007/978-3-030-01249-6_2},
	url = {http://arxiv.org/abs/1808.08319},
	author = {Hoda{\v n}, Tom{\'a}{\v s} and Michel, Frank and Brachmann, Eric and Kehl, Wadim and Buch, Anders Glent and Kraft, Dirk and Drost, Bertram and Vidal, Joel and Ihrke, Stephan and Zabulis, Xenophon and Sahin, Caner and Manhardt, Fabian and Tombari, Federico and Kim, Tae Kyun and Matas, Ji{\v r}{\'\i} and Carsten Rother}
}
@article {6254,
	title = {Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats},
	journal = {Sage Journals},
	volume = { Journal of Cerebral Blood Flow \& Metabolism},
	year = {2018},
	month = {05/18},
	abstract = {The majority of stroke patients develop post-stroke fatigue, a symptom which impairs motivation and diminishes the success of rehabilitative interventions. We show that large cortical strokes acutely reduce activity levels in rats for 1{\textendash}2 weeks as a physiological response paralleled by signs of systemic inflammation. Rats were exposed early (1{\textendash}2 weeks) or late (3{\textendash}4 weeks after stroke) to an individually monitored enriched environment to stimulate self-controlled high-intensity sensorimotor training. A group of animals received Anti-Nogo antibodies for the first two weeks after stroke, a neuronal growth promoting immunotherapy already in clinical trials. Early exposure to the enriched environment resulted in poor outcome: Training intensity was correlated to enhanced systemic inflammation and functional impairment. In contrast, animals starting intense sensorimotor training two weeks after stroke preceded by the immunotherapy revealed better recovery with functional outcome positively correlated to the training intensity and the extent of re-innervation of the stroke denervated cervical hemi-cord. Our results suggest stroke-induced fatigue as a biological purposeful reaction of the organism during neuronal remodeling, enabling new circuit formation which will then be stabilized or pruned in the subsequent rehabilitative training phase. However, intense training too early may lead to wrong connections and is thus less effective.},
	url = {http://journals.sagepub.com/doi/abs/10.1177/0271678X18777661},
	author = {Anna-Sophia Wahl and Erlebach, E. and Biagio Brattoli and Uta B{\"u}chler and Kaiser, J. and Ineichen, V. B. and Alice C. Mosberger and Schneeberger, S. and Imobersteg, S. and Wieckhorst, M. and Stirn, M. and Schroeter, A. and Bj{\"o}rn Ommer and M. E. Schwab}
}
@conference {DBLP:conf/aaai/HallerSS18,
	title = {Exact MAP-Inference by Confining Combinatorial Search With LP Relaxation},
	booktitle = {Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018},
	year = {2018},
	publisher = {AAAI Press},
	organization = {AAAI Press},
	url = {https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16379},
	author = {Stefan Haller and Paul Swoboda and Bogdan Savchynskyy},
	editor = {Sheila A. McIlraith and Kilian Q. Weinberger}
}
@proceedings {6309,
	title = {Geometric Image Synthesis},
	year = {2018},
	author = {Hassan Abu Alhaija and Mustikovela, S K and Geiger, A and Carsten Rother}
}
@proceedings {6310,
	title = {iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects},
	year = {2018},
	author = {Omid Hosseini Jafari and Mustikovela, S K and Pertsch, K and E Brachmann and Carsten Rother}
}
@conference {6250,
	title = {Learning to Forecast Pedestrian Intention from Pose Dynamics},
	booktitle = {Intelligent Vehicles, IEEE, 2018},
	year = {2018},
	author = {Omar Ghori and Radek Mackowiak and Miguel Bautista and Niklas Beuter and Lucas Drumond and Ferran Diego and Bj{\"o}rn Ommer}
}
@article {erb2017mathematical,
	title = {Mathematical Analysis of the 1D Model and Reconstruction Schemes for Magnetic Particle Imaging},
	journal = {Inverse Problems},
	volume = {34},
	year = {2018},
	author = {Erb, Wolfgang and Andreas Weinmann and Ahlborg, Mandy and Christina Brandt and Bringout, Gael and Buzug, Thorsten M and Frikel, J{\"u}rgen and Kaethner, Christian and Tobias Knopp and M{\"a}rz, Thomas and M{\"o}ddel, Martin and Martin Storath and Alexander Weber}
}
@conference {6282,
	title = {Towards Learning a Realistic Rendering of Human Behavior},
	booktitle = {European Conference on Computer Vision (ECCV - HBUGEN)},
	year = {2018},
	abstract = {Realistic rendering of human behavior is of great interest for applications such as video animations, virtual reality and more generally, gaming engines. Commonly animations of persons performing actions are rendered by articulating explicit 3D models based on sequences of coarse body shape representations simulating a certain behavior. While the simulation of natural behavior can be efficiently learned from common video data, the corresponding 3D models are typically designed in manual, laborious processes or reconstructed from costly (multi-)sensor data. In this work, we present an approach towards a holistic learning framework for rendering human behavior in which all components are learned from easily available data. We utilize motion capture data to generate realistic generations which can be controlled by a user and learn to render characters using only RGB camera data. Our experiments show that we can further improve data efficiency by training on multiple characters at the same time. Overall our approach shows a completely new path towards easily available, personalized avatar creation.},
	author = {Patrick Esser and Johannes Haux and Timo Milbich and Bj{\"o}rn Ommer}
}
@article {Vianello-etal-2017-TM,
	title = {3D reconstruction by a combined structure tensor and Hough transform light field approach},
	journal = {tm - Technisches Messen},
	year = {2017},
	abstract = {Disparity estimation using the structure tensor is a local approach to determine orientation in Epipolar Plane Images. A global extension would lead to more precise and robust estimations. In this work, a novel algorithm for 3D reconstruction from linear light fields is proposed. This method uses a modified version of the Progressive Probabilistic Hough Transform to extract orientations from Epipolar Plane Images, allowing to achieve high quality disparity maps. To this aim, the structure tensor estimates are used to speed up computation and improve the disparity estimation near occlusion boundaries. The new algorithm is evaluated on both synthetic and real light field datasets, and compared with classical local disparity estimation techniques based on the structure tensor.},
	doi = {10.1515/teme-2017-0010},
	author = {Alessandro Vianello and Giulio Manfredi and Maximilian Diebold and Bernd J{\"a}hne}
}
@conference {Alhaija2017,
	title = {Augmented reality meets deep learning for car instance segmentation in urban scenes},
	booktitle = {British Machine Vision Conference 2017, BMVC 2017},
	year = {2017},
	abstract = {The success of deep learning in computer vision is based on the availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Unfortunately, creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment these images with virtual objects. This allows us to create realistic composite images which exhibit both realistic background appearance as well as a large number of complex object arrangements. In contrast to modeling complete 3D environments, our data augmentation approach requires only a few user interactions in combination with 3D shapes of the target object category. We demonstrate the utility of the proposed approach for training a state-of-the-art high-capacity deep model for semantic instance segmentation. In particular, we consider the task of segmenting car instances on the KITTI dataset which we have annotated with pixel-accurate ground truth. Our experiments demonstrate that models trained on augmented imagery generalize better than those trained on synthetic data or models trained on limited amounts of annotated real data.},
	isbn = {190172560X},
	author = {Hassan Abu Alhaija and Mustikovela, Siva Karthik and Mescheder, Lars and Geiger, Andreas and Carsten Rother}
}
@conference {Behl2017,
	title = {Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2017-Octob},
	year = {2017},
	pages = {2593{\textendash}2602},
	abstract = {Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e.g., at texture-less or reflective surfaces. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estimation problems by exploiting recognition. In particular, we investigate the importance of recognition granularity, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions. We compute these cues using CNNs trained on a newly annotated dataset of stereo images and integrate them into a CRF-based model for robust 3D scene flow estimation - an approach we term Instance Scene Flow. We analyze the importance of each recognition cue in an ablation study and observe that the instance segmentation cue is by far strongest, in our setting. We demonstrate the effectiveness of our method on the challenging KITTI 2015 scene flow benchmark where we achieve state-of-the-art performance at the time of submission.},
	isbn = {9781538610329},
	issn = {15505499},
	doi = {10.1109/ICCV.2017.281},
	author = {Behl, Aseem and Omid Hosseini Jafari and Mustikovela, Siva Karthik and Hassan Abu Alhaija and Carsten Rother and Geiger, Andreas}
}
@conference {Behl2017a,
	title = {Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2017-Octob},
	year = {2017},
	pages = {2593{\textendash}2602},
	abstract = {Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e.g., at texture-less or reflective surfaces. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estimation problems by exploiting recognition. In particular, we investigate the importance of recognition granularity, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions. We compute these cues using CNNs trained on a newly annotated dataset of stereo images and integrate them into a CRF-based model for robust 3D scene flow estimation - an approach we term Instance Scene Flow. We analyze the importance of each recognition cue in an ablation study and observe that the instance segmentation cue is by far strongest, in our setting. We demonstrate the effectiveness of our method on the challenging KITTI 2015 scene flow benchmark where we achieve state-of-the-art performance at the time of submission.},
	isbn = {9781538610329},
	issn = {15505499},
	doi = {10.1109/ICCV.2017.281},
	author = {Behl, Aseem and Omid Hosseini Jafari and Mustikovela, Siva Karthik and Hassan Abu Alhaija and Carsten Rother and Geiger, Andreas}
}
@conference {Schlesinger2017,
	title = {Crowd sourcing image segmentation with iaSTAPLE},
	booktitle = {Proceedings - International Symposium on Biomedical Imaging},
	year = {2017},
	pages = {401{\textendash}405},
	abstract = {We propose a novel label fusion technique as well as a crowdsourcing protocol to efficiently obtain accurate epithelial cell segmentations from non-expert crowd workers. Our label fusion technique simultaneously estimates the true segmentation, the performance levels of individual crowd workers, and an image segmentation model in the form of a pairwise Markov random field. We term our approach image-aware STAPLE (iaSTAPLE) since our image segmentation model seamlessly integrates into the well-known and widely used STAPLE approach. In an evaluation on a light microscopy dataset containing more than 5000 membrane labeled epithelial cells of a fly wing, we show that iaSTAPLE outperforms STAPLE in terms of segmentation accuracy as well as in terms of the accuracy of estimated crowd worker performance levels, and is able to correctly segment 99\% of all cells when compared to expert segmentations. These results show that iaSTAPLE is a highly useful tool for crowd sourcing image segmentation.},
	keywords = {Crowdsourcing, Epithelial cell segmentation, IaSTAPLE, Markovian Random Fields},
	isbn = {9781509011711},
	issn = {19458452},
	doi = {10.1109/ISBI.2017.7950547},
	author = {Schlesinger, Dmitrij and Jug, Florian and Myers, Gene and Carsten Rother and Kainmueller, Dagmar}
}
@conference {Brachmann2017,
	title = {DSAC - Differentiable RANSAC for camera localization},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	month = {nov},
	pages = {2492{\textendash}2500},
	abstract = {RANSAC is an important algorithm in robust optimization and a central building block for many computer vision applications. In recent years, traditionally hand-crafted pipelines have been replaced by deep learning pipelines, which can be trained in an end-to-end fashion. However, RANSAC has so far not been used as part of such deep learning pipelines, because its hypothesis selection procedure is non-differentiable. In this work, we present two different ways to overcome this limitation. The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w.r.t. to all learnable parameters. We call this approach DSAC, the differentiable counterpart of RANSAC. We apply DSAC to the problem of camera localization, where deep learning has so far failed to improve on traditional approaches. We demonstrate that by directly minimizing the expected loss of the output camera poses, robustly estimated by RANSAC, we achieve an increase in accuracy. In the future, any deep learning pipeline can use DSAC as a robust optimization component.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.267},
	url = {http://arxiv.org/abs/1611.05705},
	author = {Brachmann, Eric and Krull, Alexander and Nowozin, Sebastian and Shotton, Jamie and Michel, Frank and Gumhold, Stefan and Carsten Rother}
}
@conference {Michel2017,
	title = {Global hypothesis generation for 6D object pose estimation},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	month = {dec},
	pages = {115{\textendash}124},
	abstract = {This paper addresses the task of estimating the 6D pose of a known 3D object from a single RGB-D image. Most modern approaches solve this task in three steps: i) Compute local features; ii) Generate a pool of pose-hypotheses; iii) Select and refine a pose from the pool. This work focuses on the second step. While all existing approaches generate the hypotheses pool via local reasoning, e.g. RANSAC or Hough-voting, we are the first to show that global reasoning is beneficial at this stage. In particular, we formulate a novel fully-connected Conditional Random Field (CRF) that outputs a very small number of pose-hypotheses. Despite the potential functions of the CRF being non-Gaussian, we give a new and efficient two-step optimization procedure, with some guarantees for optimality. We utilize our global hypotheses generation procedure to produce results that exceed state-of-the-art for the challenging "Occluded Object Dataset".},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.20},
	url = {http://arxiv.org/abs/1612.02287},
	author = {Michel, Frank and Kirillov, Alexander and Brachmann, Eric and Krull, Alexander and Gumhold, Stefan and Savchynskyy, Bogdan and Carsten Rother}
}
@article {6210,
	title = {An Objective Comparison of Cell Tracking Algorithms},
	journal = {Nature Methods},
	volume = {14},
	year = {2017},
	pages = {1141-1152},
	doi = {10.1038/NMETH.447},
	author = {Ulman, V. and Ma{\v s}ka, M. and Magnusson, K .E. G. and Ronneberger, O. and Haubold, C. and Harder, N. and Matula, P. and Matula, P. and Svoboda, D. and Radojevic, M. and Smal, I. and Karl Rohr and Jald{\'e}n, J. and Blau, H. M. and Dzyubachyk, O. and Lelieveldt, B. and Xiao, P. and Li, Y. and Cho, S-Y. and Dufour, A. and Olivo-Marin, J. C. and Reyes-Aldasoro, C. C. and Solis-Lemus, J. A. and Bensch, R. and Brox, T. and Stegmaier, J. and Mikut, R. and Wolf, S. and Fred A. Hamprecht and Esteves, T. and Quelhas, P. and Demirel, {\"O}. and Malstr{\"o}m, L. and Jug, F. and Toman{\v c}{\'a}k, P. and Meijering, E. and Mu{\~n}oz-Barrutia, A. and Kozubek, M. and Ortiz-de-Solorzano, C.}
}
@article {6188,
	title = {Optogenetically stimulating the intact corticospinal tract post-stroke restores motor control through regionalized functional circuit formation},
	journal = {Nature Communications},
	year = {2017},
	pages = {(ASW \& UB contributed equally; BO and MES contributed equally)},
	doi = {10.1038/s41467-017-01090-6},
	url = {https://www.nature.com/articles/s41467-017-01090-6},
	author = {Anna-Sophia Wahl and Uta B{\"u}chler and A. Br{\"a}ndli and Biagio Brattoli and S. Musall and H. Kasper and B.V. Ineichen and F. Helmchen and Bj{\"o}rn Ommer and M. E. Schwab}
}
@conference {Krull2017,
	title = {PoseAgent: Budget-constrained 6D object pose estimation via reinforcement learning},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	month = {dec},
	pages = {2566{\textendash}2574},
	abstract = {State-of-the-art computer vision algorithms often achieve efficiency by making discrete choices about which hypotheses to explore next. This allows allocation of computational resources to promising candidates, however, such decisions are non-differentiable. As a result, these algorithms are hard to train in an end-to-end fashion. In this work we propose to learn an efficient algorithm for the task of 6D object pose estimation. Our system optimizes the parameters of an existing state-of-the art pose estimation system using reinforcement learning, where the pose estimation system now becomes the stochastic policy, parametrized by a CNN. Additionally, we present an efficient training algorithm that dramatically reduces computation time. We show empirically that our learned pose estimation procedure makes better use of limited resources and improves upon the state-of-the-art on a challenging dataset. Our approach enables differentiable end-to-end training of complex algorithmic pipelines and learns to make optimal use of a given computational budget.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.275},
	url = {http://arxiv.org/abs/1612.03779},
	author = {Krull, Alexander and Brachmann, Eric and Nowozin, Sebastian and Michel, Frank and Shotton, Jamie and Carsten Rother}
}
@booklet {Massiceti,
	title = {Random Forests versus Neural Networks - What{\textquoteright}s best for camera location},
	year = {2017},
	abstract = {This work addresses the task of camera localiza-tion in a known 3D scene given a single input RGB image. State-of-the-art approaches accomplish this in two steps: firstly, regressing for every pixel in the image its 3D scene coordinate and subsequently, using these coordinates to estimate the final 6D camera pose via RANSAC. To solve the first step, Random Forests (RFs) are typically used. On the other hand, Neural Networks (NNs) reign in many dense regression tasks, but are not test-time efficient. We ask the question: which of the two is best for camera localization? To address this, we make two method contributions: (1) a test-time efficient NN architecture which we term a ForestNet that is derived and initialized from a RF, and (2) a new fully-differentiable robust averaging technique for regression ensembles which can be trained end-to-end with a NN. Our experimental findings show that for scene coordinate regression, traditional NN architectures are superior to test-time efficient RFs and ForestNets, however, this does not translate to final 6D camera pose accuracy where RFs and ForestNets perform slightly better. To summarize, our best method, a ForestNet with a robust average, which has an equivalent fast and lightweight RF, improves over the state-of-the-art for camera localization on the 7-Scenes dataset [1]. While this work focuses on scene coordinate regression for camera localization, our innovations may also be applied to other continuous regression tasks.},
	author = {Massiceti, Daniela and Krull, Alexander and Brachmann, Eric and Carsten Rother and Torr, Philip H S}
}
@conference {Markowsky2017,
	title = {Segmentation of cell structure using model-based set covering with iterative reweighting},
	booktitle = {Proc. ISBI},
	year = {2017},
	author = {Markowsky, P. and Reith, S. and Zuber, T.E. and K{\"o}nig, R. and Rohr, K. and Schn{\"o}rr, C.}
}
@conference {6187,
	title = {Unsupervised Video Understanding by Reconciliation of Posture Similarities},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
	year = {2017},
	url = {https://hciweb.iwr.uni-heidelberg.de/compvis/research/tmilbich_iccv17},
	author = {Timo Milbich and Miguel Bautista and Ekaterina Sutter and Bj{\"o}rn Ommer}
}
@proceedings {schilling-etal-2016-ForumBildverarbeitung,
	title = {3D Reconstruction by a Combined Structure Tensor and Hough Transform Light-Field Approach},
	year = {2016},
	abstract = {Disparity estimation using the structure tensor is a local approach to determine orientation in Epipolar Plane Images. A global extension would lead to more precise and robust estimations. In this work, a novel algorithm for 3D reconstruction from linear light fields is porposed. It uses a modified Progressive Probabilistic Hough Transform, in combination with the structure tensor, to extract orientations from Epipolar Plane Images edge maps, allowing to achieve high quality disparity maps.},
	isbn = {978-3-7315-0587-7},
	doi = {10.5445/KSP/1000059899},
	url = {https://doi.org/10.5445/KSP/1000059899},
	author = {Alessandro Vianello and Giulio Manfredi and Maximilian Diebold and Bernd J{\"a}hne}
}
@conference {Mustikovela2016,
	title = {Can ground truth label propagation from video help semantic segmentation?},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9915 LNCS},
	year = {2016},
	pages = {804{\textendash}820},
	abstract = {For state-of-the-art semantic segmentation task, training convolutional neural networks (CNNs) requires dense pixelwise ground truth (GT) labeling, which is expensive and involves extensive human effort. In this work, we study the possibility of using auxiliary ground truth, so-called pseudo ground truth (PGT) to improve the performance. The PGT is obtained by propagating the labels of a GT frame to its subsequent frames in the video using a simple CRF-based, cue integration framework. Our main contribution is to demonstrate the use of noisy PGT along with GT to improve the performance of a CNN. We perform a systematic analysis to find the right kind of PGT that needs to be added along with the GT for training a CNN. In this regard, we explore three aspects of PGT which influence the learning of a CNN: (i) the PGT labeling has to be of good quality; (ii) the PGT images have to be different compared to the GT images; (iii) the PGT has to be trusted differently than GT. We conclude that PGT which is diverse from GT images and has good quality of labeling can indeed help improve the performance of a CNN. Also, when PGT is multiple folds larger than GT, weighing down the trust on PGT helps in improving the accuracy. Finally, We show that using PGT along with GT, the performance of Fully Convolutional Network (FCN) on Camvid data is increased by 2.7\% on IoU accuracy. We believe such an approach can be used to train CNNs for semantic video segmentation where sequentially labeled image frames are needed. To this end, we provide recommendations for using PGT strategically for semantic segmentation and hence bypass the need for extensive human efforts in labeling.},
	isbn = {9783319494081},
	issn = {16113349},
	doi = {10.1007/978-3-319-49409-8_66},
	author = {Mustikovela, Siva Karthik and Yang, Michael Ying and Carsten Rother}
}
@article {kleesiek_16_deep,
	title = {Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.},
	journal = {NeuroImage},
	volume = {129},
	year = {2016},
	pages = {460-469},
	doi = {10.1016/j.neuroimage.2016.01.024},
	author = {Kleesiek, J and Urban, G and Hubert, A and Schwarz, D and Maier-Hein, K and M. Bendszus and A. Biller}
}
@article {6119,
	title = {Imagining the future of bioimage analysis},
	journal = {Nature Biotechnology},
	volume = {34},
	year = {2016},
	pages = {1250-1255},
	doi = {10.1038/nbt.3722},
	author = {Meijering, E and Carpenter, A E and Peng, Hanchuan and Fred A. Hamprecht and Olivo-Marin, J}
}
@conference {Mund2016,
	title = {Introducing LiDAR Point Cloud-based Object Classification for Safer Apron Operations},
	booktitle = {International Symposium on Enhanced Solutions for Aircraft and Vehicle Surveillance Applications},
	year = {2016},
	abstract = {Current procedures for conventional and remote airport ground control still rely on the direct (camera-) view. Despite further support by different Radar applications occasional shortcomings in the awareness of the responsible controllers may occur, particularly under adverse weather conditions, giving rise to capacity backlogs, incidents and accidents. As Laser scanners and computer vision algorithms have reached new performance levels in recent years, we proposed a novel concept for complete and independent airport apron surveillance based on LiDAR 3D point data. In this paper we extend our object detection/segmentation technique by addressing object classification in LiDAR 3D scans. We hereby enable LiDAR{\textquoteleft}s unique capability to classify non-cooperative objects by means of a single sensor and learned model knowledge. Our technique was able to classify and to estimate the poses of an Airbus A319-100 and a Boeing B737-700 parked on the airport apron. In the future we will enhance our classification technique to a wider range of objects including moving ground vehicles and pedestrians.},
	keywords = {- lidar, 3d point cloud, aircraft, airport ground surveillance, apron control, apron control rely on, classification, current legacy procedures for, laser scanning, pose estimation},
	url = {https://goo.gl/28Yoqh},
	author = {Mund, Johannes and Michel, Frank and Dieke-Meier, Franziska and Fricke, Hartmut and Meyer, Lothar and Carsten Rother}
}
@conference {Pinggera2016,
	title = {Lost and found: Detecting small road hazards for self-driving vehicles},
	booktitle = {IEEE International Conference on Intelligent Robots and Systems},
	volume = {2016-Novem},
	year = {2016},
	pages = {1099{\textendash}1106},
	abstract = {Detecting small obstacles on the road ahead is a critical part of the driving task which has to be mastered by fully autonomous cars. In this paper, we present a method based on stereo vision to reliably detect such obstacles from a moving vehicle. The proposed algorithm performs statistical hypothesis tests in disparity space directly on stereo image data, assessing freespace and obstacle hypotheses on independent local patches. This detection approach does not depend on a global road model and handles both static and moving obstacles. For evaluation, we employ a novel lost-cargo image sequence dataset comprising more than two thousand frames with pixelwise annotations of obstacle and free-space and provide a thorough comparison to several stereo-based baseline methods. The dataset will be made available to the community to foster further research on this important topic4. The proposed approach outperforms all considered baselines in our evaluations on both pixel and object level and runs at frame rates of up to 20 Hz on 2 mega-pixel stereo imagery. Small obstacles down to the height of 5 cm can successfully be detected at 20 m distance at low false positive rates.},
	isbn = {9781509037629},
	issn = {21530866},
	doi = {10.1109/IROS.2016.7759186},
	url = {http://www.6d-vision.com/lostandfounddataset},
	author = {Pinggera, Peter and Ramos, Sebastian and Gehrig, Stefan and Franke, Uwe and Carsten Rother and Mester, Rudolf}
}
@conference {Richmond2016,
	title = {Mapping auto-context decision forests to deep convnets for semantic segmentation},
	booktitle = {British Machine Vision Conference 2016, BMVC 2016},
	volume = {2016-Septe},
	year = {2016},
	month = {jul},
	pages = {144.1{\textendash}144.12},
	abstract = {We consider the task of pixel-wise semantic segmentation given a small set of labeled training images. Among two of the most popular techniques to address this task are Random Forests (RF) and Neural Networks (NN). In this work, we explore the relationship between two special forms of these techniques: stacked RFs (namely Auto-context) and deep Convolutional Neural Networks (ConvNet). Our main contribution is to show that Auto-context can be mapped to a deep ConvNet with novel architecture, and thereby trained end-to-end. This mapping can be viewed as an intelligent initialization of a deep ConvNet, enabling training even in the face of very limited amounts of training data. We also demonstrate an approximate mapping back from the refined ConvNet to a second stacked RF, with improved performance over the original. We experimentally verify that these mappings outperform stacked RFs for two different applications in computer vision and biology: Kinect-based body part labeling from depth images, and somite segmentation in microscopy images of developing zebrafish.},
	doi = {10.5244/C.30.144},
	url = {http://arxiv.org/abs/1507.07583},
	author = {Richmond, David L. and Kainmueller, Dagmar and Yang, Michael Y. and Myers, Eugene W. and Carsten Rother}
}
@conference {Richmond2016a,
	title = {Mapping auto-context decision forests to deep convnets for semantic segmentation},
	booktitle = {British Machine Vision Conference 2016, BMVC 2016},
	volume = {2016-Septe},
	year = {2016},
	pages = {144.1{\textendash}144.12},
	abstract = {We consider the task of pixel-wise semantic segmentation given a small set of labeled training images. Among two of the most popular techniques to address this task are Random Forests (RF) and Neural Networks (NN). In this work, we explore the relationship between two special forms of these techniques: stacked RFs (namely Auto-context) and deep Convolutional Neural Networks (ConvNet). Our main contribution is to show that Auto-context can be mapped to a deep ConvNet with novel architecture, and thereby trained end-to-end. This mapping can be viewed as an intelligent initialization of a deep ConvNet, enabling training even in the face of very limited amounts of training data. We also demonstrate an approximate mapping back from the refined ConvNet to a second stacked RF, with improved performance over the original. We experimentally verify that these mappings outperform stacked RFs for two different applications in computer vision and biology: Kinect-based body part labeling from depth images, and somite segmentation in microscopy images of developing zebrafish.},
	doi = {10.5244/C.30.144},
	url = {https://github.com/BVLC/caffe/wiki/Model-Zoo\#fcn},
	author = {Richmond, David L and Kainmueller, Dagmar and Yang, Michael Y and Myers, Eugene W and Carsten Rother}
}
@conference {Richmond2016b,
	title = {Mapping auto-context decision forests to deep convnets for semantic segmentation},
	booktitle = {British Machine Vision Conference 2016, BMVC 2016},
	volume = {2016-Septe},
	year = {2016},
	pages = {144.1{\textendash}144.12},
	abstract = {We consider the task of pixel-wise semantic segmentation given a small set of labeled training images. Among two of the most popular techniques to address this task are Random Forests (RF) and Neural Networks (NN). In this work, we explore the relationship between two special forms of these techniques: stacked RFs (namely Auto-context) and deep Convolutional Neural Networks (ConvNet). Our main contribution is to show that Auto-context can be mapped to a deep ConvNet with novel architecture, and thereby trained end-to-end. This mapping can be viewed as an intelligent initialization of a deep ConvNet, enabling training even in the face of very limited amounts of training data. We also demonstrate an approximate mapping back from the refined ConvNet to a second stacked RF, with improved performance over the original. We experimentally verify that these mappings outperform stacked RFs for two different applications in computer vision and biology: Kinect-based body part labeling from depth images, and somite segmentation in microscopy images of developing zebrafish.},
	doi = {10.5244/C.30.144},
	author = {Richmond, David L and Kainmueller, Dagmar and Yang, Michael Y and Myers, Eugene W and Carsten Rother}
}
@conference {Brachmann2016,
	title = {Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	volume = {2016-Decem},
	year = {2016},
	pages = {3364{\textendash}3372},
	abstract = {In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we show that a single RGB image is sufficient to achieve visually convincing results. Our key concept is to model and exploit the uncertainty of the system at all stages of the processing pipeline. The uncertainty comes in the form of continuous distributions over 3D object coordinates and discrete distributions over object labels. We give three technical contributions. Firstly, we develop a regularized, auto-context regression framework which iteratively reduces uncertainty in object coordinate and object label predictions. Secondly, we introduce an efficient way to marginalize object coordinate distributions over depth. This is necessary to deal with missing depth information. Thirdly, we utilize the distributions over object labels to detect multiple objects simultaneously with a fixed budget of RANSAC hypotheses. We tested our system for object pose estimation and camera localization on commonly used data sets. We see a major improvement over competing systems.},
	isbn = {9781467388504},
	issn = {10636919},
	doi = {10.1109/CVPR.2016.366},
	author = {Brachmann, Eric and Michel, Frank and Krull, Alexander and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
@conference {Brachmann2016a,
	title = {Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	volume = {2016-Decem},
	year = {2016},
	pages = {3364{\textendash}3372},
	abstract = {In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we show that a single RGB image is sufficient to achieve visually convincing results. Our key concept is to model and exploit the uncertainty of the system at all stages of the processing pipeline. The uncertainty comes in the form of continuous distributions over 3D object coordinates and discrete distributions over object labels. We give three technical contributions. Firstly, we develop a regularized, auto-context regression framework which iteratively reduces uncertainty in object coordinate and object label predictions. Secondly, we introduce an efficient way to marginalize object coordinate distributions over depth. This is necessary to deal with missing depth information. Thirdly, we utilize the distributions over object labels to detect multiple objects simultaneously with a fixed budget of RANSAC hypotheses. We tested our system for object pose estimation and camera localization on commonly used data sets. We see a major improvement over competing systems.},
	isbn = {9781467388504},
	issn = {10636919},
	doi = {10.1109/CVPR.2016.366},
	author = {Brachmann, Eric and Michel, Frank and Krull, Alexander and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
@article {6033,
	title = {Virtual Raters for Reproducible and Objective Assessments in Radiology},
	journal = {Nature Scientific Reports},
	volume = {6},
	year = {2016},
	doi = {10.1038/srep25007},
	author = {Kleesiek, J. and Petersen, J. and D{\"o}ring, M. and Maier-Hein, K. and Ullrich K{\"o}the and Wick, W. and Fred A. Hamprecht and M. Bendszus and A. Biller}
}
@article {Gianniotis2015,
	title = {Approximate variational inference based on a finite sample of Gaussian latent variables},
	journal = {Patt.~Anal.~Appl.},
	year = {2015},
	doi = {10.1007/s10044-015-0496-9},
	author = {Gianniotis, N. and Christoph Schn{\"o}rr and Molkenthin, C. and Bora, S.S.}
}
@conference {Honauer-etal-2015-ICCV,
	title = {The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms},
	booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
	year = {2015},
	month = {December},
	abstract = {@InProceedings{Honauer_2015_ICCV,
author = {Honauer, Katrin and Maier-Hein, Lena and Kondermann, Daniel},
title = {The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
} },
	author = {Katrin Honauer and Lena Maier-Hein and Daniel Kondermann}
}
@conference {Krull2015,
	title = {Learning analysis-by-synthesis for 6d pose estimation in RGB-D images},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2015 Inter},
	year = {2015},
	pages = {954{\textendash}962},
	abstract = {Analysis-by-synthesis has been a successful approach for many tasks in computer vision, such as 6D pose estimation of an object in an RGB-D image which is the topic of this work. The idea is to compare the observation with the output of a forward process, such as a rendered image of the object of interest in a particular pose. Due to occlusion or complicated sensor noise, it can be difficult to perform this comparison in a meaningful way. We propose an approach that "learns to compare", while taking these difficulties into account. This is done by describing the posterior density of a particular object pose with a convolutional neural network (CNN) that compares observed and rendered images. The network is trained with the maximum likelihood paradigm. We observe empirically that the CNN does not specialize to the geometry or appearance of specific objects. It can be used with objects of vastly different shapes and appearances, and in different backgrounds. Compared to state-of-the-art, we demonstrate a significant improvement on two different datasets which include a total of eleven objects, cluttered background, and heavy occlusion.},
	isbn = {9781467383912},
	issn = {15505499},
	doi = {10.1109/ICCV.2015.115},
	author = {Krull, Alexander and Brachmann, Eric and Michel, Frank and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
@article {mesarchaki2015,
	title = {Measuring air{\textendash}sea gas-exchange velocities in a large-scale annular wind{\textendash}wave tank},
	journal = {Ocean Sci.},
	volume = {11},
	year = {2015},
	pages = {121--138},
	doi = {10.5194/os-11-121-2015},
	author = {Evridiki Mesarchaki and Christine Kr{\"a}uter and Kerstin Ellen Krall and Maximilian Bopp and F. Helleis and Jonathan Williams and Bernd J{\"a}hne}
}
@conference {Zheng2015,
	title = {Object proposals estimation in depth image using compact 3D shape manifolds},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9358},
	year = {2015},
	pages = {196{\textendash}208},
	abstract = {Man-made objects, such as chairs, often have very large shape variations, making it challenging to detect them. In this work we investigate the task of finding particular object shapes from a single depth image. We tackle this task by exploiting the inherently low dimensionality in the object shape variations, which we discover and encode as a compact shape space. Starting from any collection of 3D models, we first train a low dimensional Gaussian Process Latent Variable Shape Space. We then sample this space, effectively producing infinite amounts of shape variations, which are used for training. Additionally, to support fast and accurate inference, we improve the standard 3D object category proposal generation pipeline by applying a shallow convolutional neural network-based filtering stage. This combination leads to considerable improvements for proposal generation, in both speed and accuracy. We compare our full system to previous state-of-the-art approaches, on four different shape classes, and show a clear improvement.},
	isbn = {9783319249469},
	issn = {16113349},
	doi = {10.1007/978-3-319-24947-6_16},
	author = {Zheng, Shuai and Prisacariu, Victor Adrian and Averkiou, Melinos and Cheng, Ming Ming and Mitra, Niloy J and Shotton, Jamie and Torr, Philip H.S. and Carsten Rother}
}
@article {Mitra2015,
	title = {Pacific Graphics 2015 DenseCut: Densely Connected CRFs for Realtime GrabCut},
	volume = {34},
	number = {7},
	year = {2015},
	abstract = {Figure-ground segmentation from bounding box input, provided either automatically or manually, has been ex-tremely popular in the last decade and influenced various applications. A lot of research has focused on high-quality segmentation, using complex formulations which often lead to slow techniques, and often hamper practi-cal usage. In this paper we demonstrate a very fast segmentation technique which still achieves very high quality results. We propose to replace the time consuming iterative refinement of global colour models in traditional GrabCut formulation by a densely connected CRF. To motivate this decision, we show that a dense CRF implicitly models unnormalized global colour models for foreground and background. Such relationship provides insightful analysis to bridge between dense CRF and GrabCut functional. We extensively evaluate our algorithm using two famous benchmarks. Our experimental results demonstrated that the proposed algorithm achieves an order of magnitude (10{\texttimes}) speed-up with respect to the closest competitor, and at the same time achieves a considerably higher accuracy.},
	keywords = {I46 [IMAGE PROCESSING AND COMPUTER VISION], partitioning, Segmentation{\textemdash}Region growing},
	url = {http://mftp.mmcheng.net/Papers/DenseCut.pdf},
	author = {Mitra, N J and Stam, J and Xu, K and Cheng, Ming-Ming and Prisacariu, Victor Adrian and Zheng, Shuai and Torr, Philip H S and Carsten Rother}
}
@conference {Mund2015,
	title = {Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons},
	booktitle = {Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems},
	year = {2015},
	pages = {85{\textendash}94},
	abstract = {Both the current system of airport ground control and the continuous implementation efforts of A-SMGCS and Remote Tower concepts require complete and independent surveillance coverage in real-time. We believe that 3D point clouds generated by an actively scanning LiDAR system available at TU Dresden may satisfy these high standards. Nonetheless, the utilization of LiDAR sensing for airport ground surveillance purposes is extremely challenging due to the unique requirement profile in this domain. This is also the reason why existing solutions in other domains such as autonomous driving and robotics are not directly applicable for airport ground surveillance. In a first step, we developed point cloud object detection and segmentation techniques to present that new data comprehensively to the airport apron controller. In this paper, we focused on the timely detection of dislocated objects (foreign object debris, forgotten equipment etc.) as a serious cause to hazardous situations on airport movement areas. The results are promising for various reference targets. However, the detection of very small objects (e.g. socket wrench) requires more elaborate algorithms to take full advantage of the current LiDAR technology. In the future we will assess the strength of LiDAR-based surveillance in terms of the number of hazardous situations that could be avoided or safely managed by the apron controller.},
	keywords = {Airport surveillance, apron control, Apron management service, FOD, Foreign object debris, laser scanning, LiDAR, Object detection, Point cloud},
	isbn = {9781450335621},
	doi = {10.1145/2899361.2899370},
	author = {Mund, Johannes and Zouhar, Alexander and Meyer, Lothar and Fricke, Hartmut and Carsten Rother}
}
@conference {Mund2015a,
	title = {Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons},
	booktitle = {Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems},
	year = {2015},
	pages = {85{\textendash}94},
	abstract = {Both the current system of airport ground control and the continuous implementation efforts of A-SMGCS and Remote Tower concepts require complete and independent surveillance coverage in real-time. We believe that 3D point clouds generated by an actively scanning LiDAR system available at TU Dresden may satisfy these high standards. Nonetheless, the utilization of LiDAR sensing for airport ground surveillance purposes is extremely challenging due to the unique requirement profile in this domain. This is also the reason why existing solutions in other domains such as autonomous driving and robotics are not directly applicable for airport ground surveillance. In a first step, we developed point cloud object detection and segmentation techniques to present that new data comprehensively to the airport apron controller. In this paper, we focused on the timely detection of dislocated objects (foreign object debris, forgotten equipment etc.) as a serious cause to hazardous situations on airport movement areas. The results are promising for various reference targets. However, the detection of very small objects (e.g. socket wrench) requires more elaborate algorithms to take full advantage of the current LiDAR technology. In the future we will assess the strength of LiDAR-based surveillance in terms of the number of hazardous situations that could be avoided or safely managed by the apron controller.},
	keywords = {Airport surveillance, apron control, Apron management service, FOD, Foreign object debris, laser scanning, LiDAR, Object detection, Point cloud},
	isbn = {9781450335621},
	doi = {10.1145/2899361.2899370},
	author = {Mund, Johannes and Zouhar, Alexander and Meyer, Lothar and Fricke, Hartmut and Carsten Rother}
}
@conference {Michel2015,
	title = {Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression},
	year = {2015},
	pages = {181.1{\textendash}181.11},
	abstract = {In this paper, we address the problem of one shot pose estimation of articulated ob-jects from an RGB-D image. In particular, we consider object instances with the topol-ogy of a kinematic chain, i.e. assemblies of rigid parts connected by prismatic or revolute joints. This object type occurs often in daily live, for instance in the form of furniture or electronic devices. Instead of treating each object part separately we are using the rela-tionship between parts of the kinematic chain and propose a new minimal pose sampling approach. This enables us to create a pose hypothesis for a kinematic chain consist-ing of K parts by sampling K 3D-3D point correspondences. To asses the quality of our method, we gathered a large dataset containing four objects and 7000+ annotated RGB-D frames 1 . On this dataset we achieve considerably better results than a modified state-of-the-art pose estimation system for rigid objects.},
	doi = {10.5244/c.29.181},
	author = {Michel, Frank and Krull, Alexander and Brachmann, Eric and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
@inbook {Kondermann-etal-2015-ACCV,
	title = {Stereo Ground Truth with Error Bars},
	booktitle = {Computer Vision {\textendash} ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part V},
	year = {2015},
	pages = {595{\textendash}610},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Creating stereo ground truth based on real images is a measurement task. Measurements are never perfectly accurate: the depth at each pixel follows an error distribution. A common way to estimate the quality of measurements are error bars. In this paper we describe a methodology to add error bars to images of previously scanned static scenes. The main challenge for stereo ground truth error estimates based on such data is the nonlinear matching of 2D images to 3D points. Our method uses 2D feature quality, 3D point and calibration accuracy as well as covariance matrices of bundle adjustments. We sample the reference data error which is the 3D depth distribution of each point projected into 3D image space. The disparity distribution at each pixel location is then estimated by projecting samples of the reference data error on the 2D image plane. An analytical Gaussian error propagation is used to validate the results. As proof of concept, we created ground truth of an image sequence with 100 frames. Results show that disparity accuracies well below one pixel can be achieved, albeit with much large errors at depth discontinuities mainly caused by uncertain estimates of the camera location.},
	isbn = {978-3-319-16814-2},
	doi = {10.1007/978-3-319-16814-2_39},
	url = {http://dx.doi.org/10.1007/978-3-319-16814-2_39},
	author = {Daniel Kondermann and Nair, Rahul and Stephan Meister and Wolfgang Mischler and G{\"u}ssefeld, Burkhard and Katrin Honauer and Sabine Hofmann and Brenner, Claus and Bernd J{\"a}hne}
}
@article {cali_15_three-dimensional,
	title = {Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues},
	journal = {Journal of Comparative Neurology},
	volume = {524},
	year = {2015},
	pages = {23-38},
	doi = {10.1002/cne.23852},
	author = {Cali, C. and Baghabra, J. and Boges, D. J. and Holst, G. R. and Anna Kreshuk and Fred A. Hamprecht and Srinivasan, M. and Lehv{\"a}slaiho, H. and Magistretti, P. J.}
}
@incollection {Richmond2015,
	title = {Uncertainty-driven forest predictors for vertebra localization and segmentation},
	volume = {9349},
	year = {2015},
	pages = {653{\textendash}660},
	abstract = {Accurate localization, identification and segmentation of vertebrae is an important task in medical and biological image analysis. The prevailing approach to solve such a task is to first generate pixelindependent features for each vertebra, e.g. via a random forest predictor, which are then fed into an MRF-based objective to infer the optimal MAP solution of a constellation model. We abandon this static, twostage approach and mix feature generation with model-based inference in a new, more flexible, way. We evaluate our method on two data sets with different objectives. The first is semantic segmentation of a 21-part body plan of zebrafish embryos in microscopy images, and the second is localization and identification of vertebrae in benchmark human CT.},
	issn = {16113349},
	doi = {10.1007/978-3-319-24553-9_80},
	author = {Richmond, David and Kainmueller, Dagmar and Glocker, Ben and Carsten Rother and Myers, Gene}
}
@conference {Kainmueller2014,
	title = {Active graph matching for automatic joint segmentation and annotation of C. elegans},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8673 LNCS},
	number = {PART 1},
	year = {2014},
	pages = {81{\textendash}88},
	abstract = {In this work we present a novel technique we term active graph matching, which integrates the popular active shape model into a sparse graph matching problem. This way we are able to combine the benefits of a global, statistical deformation model with the benefits of a local deformation model in form of a second-order random field. We present a new iterative energy minimization technique which achieves empirically good results. This enables us to exceed state-of-the art results for the task of annotating nuclei in 3D microscopic images of C. elegans. Furthermore with the help of the generalized Hough transform we are able to jointly segment and annotate a large set of nuclei in a fully automatic fashion for the first time. {\textcopyright} 2014 Springer International Publishing.},
	isbn = {9783319104034},
	issn = {16113349},
	doi = {10.1007/978-3-319-10404-1_11},
	author = {Kainmueller, Dagmar and Jug, Florian and Carsten Rother and Myers, Gene}
}
@conference {tek_14_automated,
	title = {Automated Cell Nucleus Detection for Large-Volume Electron Microscopy of Neural Tissue},
	booktitle = {ISBI. Proceedings},
	year = {2014},
	note = {1},
	pages = {69-72},
	doi = {10.1109/ISBI.2014.6867811},
	author = {Tek, B. F. and Thorben Kr{\"o}ger and Mikula, S. and Fred A. Hamprecht}
}
@conference {maierhein2014,
	title = {Can masses of non-experts train highly accurate image classifiers? A crowdsourcing approach to instrument segmentation in laparoscopic images},
	booktitle = {MICCAI},
	year = {2014},
	author = {Lena Maier-Hein and Sven Mersmann and Daniel Kondermann and Bodenstedt, S. and Sanchez, A. and C. Stock and Kenngott, H. and Eisenmann, M. and Speidel, S.}
}
@phdthesis {meister2014,
	title = {On Creating Reference Data for Performance Analysis in Image Processing},
	year = {2014},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	url = {http://www.ub.uni-heidelberg.de/archiv/16193},
	author = {Stephan Meister}
}
@phdthesis {meister2014,
	title = {On Creating Reference Data for Performance Analysis in Image Processing},
	volume = {Dissertation},
	year = {2014},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ. Heidelberg},
	type = {phdDissertation},
	doi = {10.11588/heidok.00016193},
	author = {Stephan Nicolas Robert Meister}
}
@conference {maierhein2014a,
	title = {Crowdsourcing for reference correspondence generation in endoscopic images},
	booktitle = {MICCAI},
	year = {2014},
	author = {Lena Maier-Hein and Sven Mersmann and Daniel Kondermann and C. Stock and Kenngott, H. and Sanchez, A. and Wagner, M. and Preukschas, A. and Wekerle, A. -L. and Helfert, S. and Bodenstedt, S. and Speidel, S.}
}
@article {mesarchaki2014a,
	title = {Measuring air-sea gas exchange velocities in a large scale annular wind-wave tank},
	journal = {Ocean Sci. Discuss.},
	volume = {11},
	year = {2014},
	pages = {1643--1689},
	doi = {10.5194/osd-11-1643-2014},
	author = {Evridiki Mesarchaki and Christine Kr{\"a}uter and Kerstin Ellen Krall and Maximilian Bopp and F. Helleis and Jonathan Williams and Bernd J{\"a}hne}
}
@article {monoroy:IVC:2014,
	title = {Morphological Analysis for Investigating Artistic Images},
	journal = {Image and Vision Computing},
	volume = {32},
	number = {6},
	year = {2014},
	pages = {414--423},
	publisher = {Elsevier},
	author = {Monroy, A. and Bell, P. and Bj{\"o}rn Ommer}
}
@conference {Marquez-Neila2014,
	title = {Non-parametric higher-order random fields for image segmentation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8694 LNCS},
	number = {PART 6},
	year = {2014},
	pages = {269{\textendash}284},
	abstract = {Models defined using higher-order potentials are becoming increasingly popular in computer vision. However, the exact representation of a general higher-order potential defined over many variables is computationally unfeasible. This has led prior works to adopt parametric potentials that can be compactly represented. This paper proposes a non-parametric higher-order model for image labeling problems that uses a patch-based representation of its potentials. We use the transformation scheme of [11, 25] to convert the higher-order potentials to a pair-wise form that can be handled using traditional inference algorithms. This representation is able to capture structure, geometrical and topological information of labels from training data and to provide more precise segmentations. Other tasks such as image denoising and reconstruction are also possible. We evaluate our method on denoising and segmentation problems with synthetic and real images. {\textcopyright} 2014 Springer International Publishing.},
	keywords = {biomedical image analysis, higher-order models, image denoising, Image segmentation, random fields, structured prediction},
	isbn = {9783319105987},
	issn = {16113349},
	doi = {10.1007/978-3-319-10599-4_18},
	author = {M{\'a}rquez-Neila, Pablo and Kohli, Pushmeet and Carsten Rother and Baumela, Luis}
}
@article {Jug2014,
	title = {Optimal joint segmentation and tracking of escherichia coli in the mother machine},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8677},
	year = {2014},
	pages = {25{\textendash}36},
	abstract = {We introduce a graphical model for the joint segmentation and tracking of E. coli cells from time lapse videos. In our setup cells are grown in narrow columns (growth channels) in a so-called {\textquotedblleft}Mother Machine{\textquotedblright} [1]. In these growth channels, cells are vertically aligned, grow and divide over time, and eventually leave the channel at the top. The model is built on a large set of cell segmentation hypotheses for each video frame that we extract from data using a novel parametric max-flow variation. Possible tracking assignments between segments across time, including cell identity mapping, cell division, and cell exit events are enumerated. Each such assignment is represented as a binary decision variable with unary costs based on image and object features of the involved segments. We find a cost-minimal and consistent solution by solving an integer linear program. We introduce a new and important type of constraint that ensures that cells exit the Mother Machine in the correct order. Our method finds a globally optimal tracking solution with an accuracy of > 95\% (1.22 times the inter-observer error) and is on average 2 - 11 times faster than the microscope produces the raw data.},
	issn = {16113349},
	doi = {10.1007/978-3-319-12289-2_3},
	author = {Jug, Florian and Pietzsch, Tobias and Kainm{\"u}ller, Dagmar and Funke, Jan and Kaiser, Matthias and van Nimwegen, Erik and Carsten Rother and Myers, Gene}
}
@article {maco_14_semiautomated,
	title = {Semiautomated Correlative 3D Electron Microscopy of In Vivo Imaged Axons and Dendrites},
	journal = {Nature Protocols},
	volume = {9},
	year = {2014},
	pages = {1354-1366},
	doi = {10.1038/nprot.2014.101},
	author = {Maco, B. and Cantoni, M. and Holtmaat, A. and Anna Kreshuk and Fred A. Hamprecht and G. W. Knott}
}
@conference {kondermann2014,
	title = {Stereo ground truth with error bars},
	booktitle = {Asian Conference on Computer Vision, ACCV 2014},
	year = {2014},
	author = {Daniel Kondermann and Nair, Rahul and Stephan Meister and Wolfgang Mischler and G{\"u}ssefeld, Burkhard and Sabine Hofmann and Brenner, Claus and Bernd J{\"a}hne}
}
@phdthesis {mischler2014,
	title = {Systematic Measurements of Bubble Induced Gas Exchange for Trace Gases with Low Solubilities},
	volume = {Dissertation},
	year = {2014},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	url = {http://www.ub.uni-heidelberg.de/archiv/17720},
	author = {Wolfgang Mischler}
}
@conference {gottfried2014,
	title = {Time of flight motion compensation revisited},
	booktitle = {International Conference on Image Processing, ICIP 2014},
	year = {2014},
	author = {Gottfried, Jens-Malte and Nair, Rahul and Stephan Meister and Christoph S. Garbe and Daniel Kondermann}
}
@conference {lenor2014,
	title = {Tracking-based visibility estimation},
	booktitle = {Pattern Recognition, 36th German Conference, GCPR 2014, M{\"u}nster, Germany, September 2-5, 2014},
	volume = {8753},
	year = {2014},
	pages = {365--376},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-11752-2_29},
	author = {Lenor, Stephan and Martini, Johannes and Bernd J{\"a}hne and Stopper, Ulrich and Weber, Stefan and Ohr, Florian},
	editor = {Jiang, Xiaoyi and Joachim Hornegger and Reinhard Koch}
}
@article {mikut_13_automated,
	title = {Automated Processing of Zebrafish Imaging Data: A Survey},
	journal = {Zebrafish},
	volume = {10 (3)},
	year = {2013},
	doi = {10.1089/zeb.2013.0886},
	author = {Mikut, R. and Dickmeis, T. and Driever, W. and Geurts, P. and Fred A. Hamprecht and Bernhard X. Kausler and Ledesma-Carbayo, M. and Marée, R. and Mikula, K. and Pantazis, P. and Ronneberger, O. and Santos, A. and Stotzka, R.}
}
@article {mersmann2013,
	title = {Calibration of time-of-flight cameras for accurate intraoperative surface reconstruction},
	journal = {Med. Phys.},
	volume = {40},
	year = {2013},
	pages = {082701},
	doi = {10.1118/1.4812889},
	author = {Sven Mersmann and Alexander Seitel and Michael Erz and Bernd J{\"a}hne and Nickel, Felix and Mieth, Markus and Mehrabi, Arianeb and Lena Maier-Hein}
}
@article {maco_13_correlative,
	title = {Correlative in vivo 2 photon and focused ion beam scanning electron microscopy of cortical neurons},
	journal = {PloS one},
	volume = {8 (2)},
	year = {2013},
	doi = {10.1371/journal.pone.0057405},
	author = {Maco, B. and Holtmaat, A. and Cantoni, M. and Anna Kreshuk and Christoph N. Straehle and Fred A. Hamprecht and G. W. Knott}
}
@phdthesis {meister_13_on,
	title = {On Creating Reference Data for Performance Analysis in Image Processing},
	year = {2013},
	note = {1},
	publisher = {University of Heidelberg},
	author = {Stephan Meister}
}
@conference {wanner2013datasets,
	title = {Datasets and Benchmarks for Densely Sampled 4D Light Fields},
	booktitle = {Vision, Modeling \& Visualization},
	year = {2013},
	pages = {225--226},
	author = {Sven Wanner and Stephan Meister and Bastian Goldl{\"u}cke}
}
@conference {lenzen_13_denoising,
	title = {Denoising Strategies for Time-of-Flight Data},
	booktitle = {Time-of-Flight Imaging: Algorithms, Sensors and Applications},
	volume = {8200},
	year = {2013},
	note = {1},
	pages = {24-25},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-44964-2_2},
	author = {Frank Lenzen and Kim, K. I. and Sch{\"a}fer, H. and Nair, R. and Stephan Meister and Florian Becker and Christoph S. Garbe},
	editor = {Grzegorzek, M. and Theobalt, C. and Andreas Kolb and Theobalt, C. and Reinhard Koch}
}
@incollection {Lenzen2013stategies,
	title = {Denoising Strategies for Time-of-Flight Data},
	volume = {8200},
	year = {2013},
	pages = {25-45},
	publisher = {Springer},
	author = {Frank Lenzen and Kim, Kwang In and Sch{\"a}fer, Henrik and Nair, Rahul and Stephan Meister and Florian Becker and Christoph S. Garbe},
	editor = {Grzegorzek, Marcin and Theobalt, Christian and Andreas Kolb and Theobalt, Christian and Reinhard Koch}
}
@conference {jaehne2013a,
	title = {Der Standard EMVA 1288 zur Charakterisierung von Kameras und Bildsensoren: von 2D- zu 3D-Kameras},
	booktitle = {Photogrammetrie, Laserscanning, Optische 3D-Messtechnik, Beitr{\"a}ge der Oldenburger 3D-Tage 2013},
	year = {2013},
	pages = {388--399},
	publisher = {Wichmann},
	organization = {Wichmann},
	url = {http://www.ub.uni-heidelberg.de/archiv/17699},
	author = {Bernd J{\"a}hne},
	editor = {Thomas Luhmann and M{\"u}ller, Christina}
}
@conference {kroeger_13_learning,
	title = {Learning to Segment Neurons with Non-local Quality Measures},
	booktitle = {MICCAI 2013. Proceedings, part II},
	volume = {8150},
	year = {2013},
	note = {1},
	pages = {419-427},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-40763-5_52},
	author = {Thorben Kr{\"o}ger and Mikula, S. and Denk, W. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {antic:HACI:2013,
	title = {Less is More: Video Trimming for Action Recognition},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision, Workshop on Understanding Human Activities: Context and Interaction},
	year = {2013},
	pages = {515--521},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Antic, B. and Timo Milbich and Bj{\"o}rn Ommer}
}
@conference {monroy:SCCH:2013,
	title = {A Morphometric Approach to Reception Analysis of Premodern Art},
	booktitle = {Scientific Computing \& Cultural Heritage},
	year = {2013},
	author = {Monroy, A. and Bell, P. and Bj{\"o}rn Ommer}
}
@conference {meister_13_simulation,
	title = {Simulation of Time-of-Flight Sensors using Global Illumination},
	booktitle = {Vision, Modeling and Visualization (VMV), 2013 International Workshop
on. Proceedings},
	year = {2013},
	pages = {33-40},
	doi = {10.2312/PE.VMV.VMV13.033-040},
	author = {Stephan Meister and Nair, R. and Daniel Kondermann}
}
@conference {berger_13_state,
	title = {A State of the Art Report on Kinect Sensor Setups in Computer Vision},
	booktitle = {Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications},
	volume = {8200},
	year = {2013},
	pages = {257-272},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-44964-2_12},
	author = {Berger, K. and Stephan Meister and Nair, R. and Daniel Kondermann},
	editor = {Grzegorzek, M. and Theobalt, C. and Andreas Kolb and Reinhard Koch}
}
@conference {nair_13_survey,
	title = {A Survey on Time-of-Flight Stereo Fusion},
	booktitle = {Time-of-Flight Imaging: Algorithms, Sensors and Applications},
	volume = {8022},
	year = {2013},
	pages = {105-127},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-44964-2_6},
	author = {Nair, R. and Ruhl, K. and Frank Lenzen and Stephan Meister and Sch{\"a}fer, H. and Christoph S. Garbe and Eisemann, M. and Daniel Kondermann},
	editor = {Grzegorzek, M. and Theobalt, C. and Andreas Kolb and Reinhard Koch}
}
@incollection {Nair2013survey,
	title = {A Survey on Time-of-Flight Stereo Fusion},
	volume = {8200},
	year = {2013},
	pages = {105-127},
	publisher = {Springer},
	author = {Nair, Rahul and Ruhl, Kai and Frank Lenzen and Stephan Meister and Sch{\"a}fer, Henrik and Christoph S. Garbe and Eisemann, Martin and Magnor, Marcus and Daniel Kondermann},
	editor = {Grzegorzek, Marcin and Theobalt, Christian and Andreas Kolb and Reinhard Koch}
}
@incollection {Nair2013survey,
	title = {A Survey on Time-of-Flight Stereo Fusion},
	volume = {8200},
	year = {2013},
	pages = {105-127},
	publisher = {Springer},
	author = {Nair, Rahul and Ruhl, Kai and Lenzen, Frank and Meister, Stephan and Sch{\"a}fer, Henrik and Garbe, Christoph S. and Eisemann, Martin and Magnor, Marcus and Kondermann, Daniel},
	editor = {Grzegorzek, Marcin and Theobalt, Christian and Koch, Reinhard and Kolb, Andreas}
}
@inbook {yarlagadda:SCCH:2013,
	title = {Towards a Computer-based Understanding of Medieval Images},
	booktitle = {Scientific Computing \& Cultural Heritage},
	year = {2013},
	pages = {89--97},
	publisher = {Springer},
	organization = {Springer},
	isbn = {978-3-642-28020-7},
	url = {http://link.springer.com/chapter/10.1007/978-3-642-28021-4_10},
	author = {Yarlagadda, P. and Monroy, A. and Bernd Carque and Bj{\"o}rn Ommer}
}
@conference {jaehne2013,
	title = {Von optischer 3D-Messtechnik zu lichtfeldbasierter Bildakquisition und -verarbeitung},
	booktitle = {Photogrammetrie, Laserscanning, Optische 3D-Messtechnik, Beitr{\"a}ge der Oldenburger 3D-Tage 2013},
	year = {2013},
	note = {eingeladener Er{\"o}ffnungsvortrag},
	pages = {1--8},
	publisher = {Wichmann},
	organization = {Wichmann},
	url = {http://www.ub.uni-heidelberg.de/archiv/17698},
	author = {Bernd J{\"a}hne},
	editor = {Thomas Luhmann and M{\"u}ller, Christina}
}
@article {marquez_13_when,
	title = {When is a confidence measure good enough?},
	journal = {submitted to CVPR 2013},
	year = {2013},
	note = {1},
	author = {Márquez-Valle, P. and Gil, D. and Hernàndez-Sabaté, A. and Daniel Kondermann}
}
@incollection {jaehne2012d,
	title = {Atmosphere-water exchange},
	volume = {1},
	year = {2012},
	pages = {594--606},
	publisher = {Springer},
	doi = {10.1007/978-1-4419-0851-3_644},
	author = {Bernd J{\"a}hne},
	editor = {Robert A. Meyers}
}
@article {zechmann_12_automated,
	title = {Automated vs. manual pattern recognition of 3D 1H MRSI data of patients with prostate cancer},
	journal = {Academic Radiology},
	volume = {19, 6},
	year = {2012},
	pages = {675-684},
	doi = {10.1016/j.acra.2012.02.014},
	author = {C. M. Zechmann and Bjoern H. Menze and B. Michael Kelm and Zamecnik, P. and Ikinger, U. and Waldherr, R. and Delorme, S. and Fred A. Hamprecht and Bachert, P.}
}
@proceedings {monroy:ECCV:2012,
	title = {Beyond Bounding-Boxes: Learning Object Shape by Model-driven Grouping},
	volume = {7574},
	year = {2012},
	pages = {582--595},
	publisher = {Springer},
	doi = {10.1007/978-3-642-33712-3_42},
	author = {Monroy, A. and Bj{\"o}rn Ommer}
}
@conference {schaefer_12_edge,
	title = {Edge Detection in Time-of-Flight Images},
	booktitle = {submitted to ECCV, 2nd Workshop on Consumer Depth Cameras for Computer
Vision},
	year = {2012},
	note = {1},
	author = {Sch{\"a}fer, H. and Frank Lenzen and Stephan Meister and Christoph S. Garbe}
}
@conference {erz2012a,
	title = {Erweiterte Sch{\"a}rfentiefe mittels Scheimpflug-Bildaufnahme und Bildfusion},
	booktitle = {Forum Bildverarbeitung},
	year = {2012},
	pages = {1--12},
	publisher = {KIT Scientific Publishing},
	organization = {KIT Scientific Publishing},
	doi = {10.5445/KSP/1000030440},
	url = {http://digbib.ubka.uni-karlsruhe.de/volltexte/1000030440},
	author = {Michael Erz and Wolfgang Mischler and Trein, Johannes and Zhuang Lin and Lay, Ralf and Bernd J{\"a}hne},
	editor = {F. Puente Leon and M. Heizmann}
}
@incollection {nair2012,
	title = {Ground truth for evaluating time of flight imaging},
	volume = {8200},
	year = {2012},
	pages = {52--74},
	publisher = {Springer},
	doi = {10.1007/978-3-642-44964-2_4},
	author = {Nair, Rahul and Stephan Meister and Lambers, Martin and Balda, Michael and Hofmann, Hannes and Andreas Kolb and Daniel Kondermann and Bernd J{\"a}hne}
}
@conference {nair_12_high,
	title = {High Accuracy TOF and Stereo Sensor Fusion at Interactive Rates},
	booktitle = {submitted to ECCV 2012},
	volume = {7584},
	year = {2012},
	note = {1},
	pages = {1-11},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-33868-7_1},
	author = {Nair, R. and Frank Lenzen and Sch{\"a}fer, H. and Stephan Meister and Christoph S. Garbe and Daniel Kondermann}
}
@conference {Nair2012highaccuracy,
	title = {High accuracy TOF and stereo sensor fusion at interactive rates},
	booktitle = {Computer Vision--ECCV 2012. Workshops and Demonstrations},
	year = {2012},
	pages = {1--11},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	author = {Nair, Rahul and Frank Lenzen and Stephan Meister and Sch{\"a}fer, Henrik and Christoph S. Garbe and Daniel Kondermann}
}
@conference {Nair2012highaccuracy,
	title = {High accuracy TOF and stereo sensor fusion at interactive rates},
	booktitle = {Computer Vision{\textendash}ECCV 2012. Workshops and Demonstrations},
	year = {2012},
	pages = {1{\textendash}11},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	author = {Nair, Rahul and Lenzen, Frank and Meister, Stephan and Sch{\"a}fer, Henrik and Garbe, Christoph S. and Kondermann, Daniel}
}
@conference {mischler2012,
	title = {Optical measurements of bubbles and spray in wind/water facilities at high wind speeds},
	booktitle = {12th International Triennial Conference on Liquid Atomization and Spray Systems 2012, Heidelberg (ICLASS 2012)},
	year = {2012},
	abstract = {An optical imaging technique for the measurement of the size and velocity distribution of bubbles and droplets is presented. This method was used in a wind-wave tank at high wind-speeds to investigate bubble populations and spray generated by breaking water-waves. These measurements help to model bubble and spray induced gas exchange between atmosphere and ocean. The setup consists of a 2040  1088 CMOS-camera (Basler acA2000-340km, 5.5 mum pixel size), which is used in a telecentric bright-field setup. Thus bubbles or droplets between the light source and the camera become visible in the images as dark disks because of light scattering. For illumination, a single high-power LED (Cree XP-E) in a telecentric illumination system is used. This LED is operated in a pulsed mode with pulse lengths smaller than 2 mus. This is the effective exposure time. In order to acquire two images with a small temporal distance, the camera is set to maximal exposure time. The first image is recorded by flashing the LED at the end of the first exposure and the second image by flashing at the beginning of the second exposure. The effective time difference of consecutive exposures was between 1 mus and twice the exposure time of the camera. The telecentricity (only rays parallel to the optical axis contribute to the images) of the optics reduces the error of the size estimation of the bubbles/droplets. In addition the position along the optical axis can be estimated by a depth from defocus method. With this information it is also possible to determine the measuring volume to estimate the bubble/spray-number density. The size range of this technique is determined by the pixel and sensor size and by the resolution of the lens. Here, a lens with a working distance of 135 mm and a magnification of 0.37 is used, so that one pixel corresponds to 15 mum in object space. With this setup a radius range of 40 mum - 5000 mum can be imaged. The main source for the error of the radius is the blurring of the edges due to defocus. Therefore the accuracy depends on the quality of telecentricity and complexity of image processing. For bubbles with a radius larger than 10 px the error is below 1 \% when using simple image processing. This error can be reduced significantly by using elaborate image processing, which is also able to detect bubbles as small as 3 px in size. Different image processing algorithms for spherical objects are presented and investigated regarding accuracy. Bubbles or droplets which show deviation from sphere shape introduce problems for the image processing. For bubbles in still water, such deviations are typically found for radii of greater than 500 mum. Capabilities and limits of the technique are addressed on the example of two experiments. Test measurements were conducted at the high wind speed wind-wave tank in Kyoto, Japan at a wind speed of 42 m/s. This experiment showed that distributions of bubbles can be measured at high wind speeds with no problems in regions, which are not near to the water surface (> 2 cm). For regions close to the interface the image processing algorithms need to be adjusted, since portions of the illumination can be occluded by waves. It is shown with an experiment in the small linear wind/wave facility in Heidelberg, that this technique also works for spray measurements. Systematic measurements of spray could not be conducted in Kyoto, since the walls of the tank were covered by a thin film of water caused by the spray, which heavily disturbed the optical path.},
	doi = {10.5281/zenodo.10957},
	author = {Wolfgang Mischler and Bernd J{\"a}hne}
}
@article {meister_12_outdoor,
	title = {Outdoor Stereo Camera System for the Generation of Real-World Benchmark
Data Sets},
	journal = {Optical Engineering},
	volume = {51},
	year = {2012},
	pages = {021107-1},
	doi = {10.1117/1.OE.51.2.021107},
	author = {Stephan Meister and Bernd J{\"a}hne and Daniel Kondermann}
}
@article {meister2012,
	title = {Outdoor stereo camera system for the generation of real-world benchmark data sets},
	journal = {Opt. Eng.},
	volume = {51},
	year = {2012},
	pages = {021107},
	doi = {10.1117/1.OE.51.2.021107},
	author = {Stephan Meister and Bernd J{\"a}hne and Daniel Kondermann}
}
@conference {kraeuter2012,
	title = {Partitioning of the Trasfer Resistance between Air and Water},
	booktitle = {SOLAS Open Science Conference, Washington State, USA},
	year = {2012},
	doi = {10.5281/zenodo.12328},
	author = {Christine Kr{\"a}uter and Kerstin E. Richter and Evridiki Mesarchaki and Roland Rocholz and Jonathan Williams and Bernd J{\"a}hne}
}
@conference {kondermann_12_on,
	title = {On Performance Analysis of Optical Flow Algorithms},
	booktitle = {Outdoor and Large-Scale Real-Worls Scene Analysis, Dagstuhl-Workshop
2011},
	volume = {LNCS},
	number = {7474},
	year = {2012},
	pages = {329-355},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-34091-8_15},
	author = {Daniel Kondermann and Steffen Abraham and W. F{\"o}rstner and Gehrig, S. and Imiya, A. and Bernd J{\"a}hne and Klose, F. and Magor, M. and Helmut Mayer and Mester, R. and Pajdla, T. and Reulke, R. and Zimmer, H.}
}
@conference {monroy:ECCV:2012,
	title = {Shaping Art with Art: Morphological Analysis for Investigating Artistic Reproductions},
	booktitle = {Proceedings of the European Conference on Computer Vision, Workshop on VISART},
	volume = {7583},
	year = {2012},
	pages = {571--580},
	publisher = {Springer},
	organization = {Springer},
	author = {Monroy, A. and Bell, P. and Bj{\"o}rn Ommer}
}
@conference {Mansfield2012,
	title = {Visibility maps for improving seam carving},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6554 LNCS},
	number = {PART 2},
	year = {2012},
	pages = {131{\textendash}144},
	abstract = {In this paper, we present a new, improved seam carving algorithm. Seam carving efficiently removes pixels from an image to produce a retargeted image. It has proved popular with users and has been used as a component in many retargeting algorithms. We introduce the visibility map, a new framework for pixel removing image editing methods. This allows us to cast retargeting as a binary graph labelling problem. We derive a general algorithm which uses seam carving operations for efficient greedy optimization of a well defined energy, and compare this with forward energy seam carving and shift map image editing. We test this method with varying parameters on a large number of images, and present an improved seam carving algorithm which can demonstrably produce better results. We draw general conclusions about pixel removing methods for retargeting and motivate future directions of research. {\textcopyright} 2012 Springer-Verlag.},
	isbn = {9783642357398},
	issn = {03029743},
	doi = {10.1007/978-3-642-35740-4_11},
	url = {http://www.adobe.com/products/photoshop/photoshopextended/features/},
	author = {Mansfield, Alex and Gehler, Peter and Van Gool, Luc and Carsten Rother}
}
@conference {meister_12_when,
	title = {When Can We Use KinectFusion for Ground Truth Acquisition?},
	booktitle = {Workshop on Color-Depth Camera Fusion in Robotics, IEEE International
Conference on Intelligent Robots and Systems},
	year = {2012},
	note = {1},
	author = {Stephan Meister and Izadi, S. and Kohli, P. and H{\"a}mmerle, M. and Carsten Rother and Daniel Kondermann}
}
@article {Meister2012,
	title = {When can we use KinectFusion for ground truth acquisition?},
	journal = {Proc Workshop on \ldots},
	year = {2012},
	pages = {3{\textendash}8},
	abstract = {Abstract{\textemdash}KinectFusion is a method for real-time capture of dense 3D geometry of the physical environment using a depth sensor. The system allows capture of a large dataset of 3D scene reconstructions at very low cost. In this paper we discuss the properties of the ...$\backslash$n},
	url = {http://meshlab.sourceforge.net/ http://www.msr-waypoint.net/en-us/um/people/pkohli/papers/mikhrk_iros_dataset_2012.pdf\%5Cnpapers3://publication/uuid/2615CF9D-C632-4E39-B1C4-B32A4A5D339C},
	author = {Meister, Stephan and Izadi, Shahram and Kohli, Pushmeet and M H{\"a}mmerle}
}
@conference {schimpf2011b,
	title = {The 2009 SOPRAN active thermography pilot experiment in the Baltic Sea},
	booktitle = {Gas Transfer at Water Surfaces 2010},
	year = {2011},
	pages = {358--367},
	doi = {10.5281/zenodo.14956},
	url = {http://hdl.handle.net/2433/156156},
	author = {Uwe Schimpf and Leila Nagel and Bernd J{\"a}hne},
	editor = {S. Komori and W. R. McGilles and R. Kurose}
}
@conference {monroy:ICIP:2011,
	title = {Beyond Straight Lines - Object Detection using Curvature},
	booktitle = {International Conference on Image Processing (ICIP)},
	year = {2011},
	publisher = {IEEE},
	organization = {IEEE},
	doi = {10.1109/ICIP.2011.6116485},
	author = {Monroy, A. and Eigenstetter, A. and Bj{\"o}rn Ommer}
}
@conference {rocholz2011a,
	title = {Combined visualization of wind waves and water surface temperature},
	booktitle = {Gas Transfer at Water Surfaces 2010},
	year = {2011},
	pages = {496--506},
	doi = {10.5281/zenodo.14957},
	url = {http://hdl.handle.net/2433/156156},
	author = {Roland Rocholz and Sven Wanner and Uwe Schimpf and Bernd J{\"a}hne},
	editor = {S. Komori and W. R. McGilles and R. Kurose}
}
@conference {kraeuter2011,
	title = {A comparative lab study of tansfer velocities of volatile tracers with widely varying solubilities},
	booktitle = {DPG Fr{\"u}hjahrstagung Dresden, Fachverband Umweltphysik},
	year = {2011},
	abstract = {Die L{\"o}slichkeit einer fl{\"u}chtigen Substanz in Wasser hat einen entscheidenden Einfluss auf den Gasaustausch zwischen Ozean und Atmosph{\"a}re. Bei Stoffen mit einer sehr hohen L{\"o}slichkeit wird der Austausch durch Diffusion in der luftseitigen Grenzschicht kontrolliert und bei solchen mit einer sehr niedrigen L{\"o}slichkeit von der wasserseitigen Grenzschicht. Bei vielen umweltrelevanten Stoffen (z.B. Aceton, Acetaldehyd, Acetonitril) ist es aber ein Wechselspiel von beiden Prozessen. Die Kombination der Prozesse ist bisher experimentell nicht untersucht worden und es gibt nur einfache Modelle, welche die Intermittenz der Prozesse ber{\"u}cksichtigen. In einem ersten Laborexperiment am Aeolotron, einem ringf{\"o}rmigen Wind-Wellen-Kanal, wurden die Transferwiderst{\"a}nde vieler Gase mit unterschiedlichen L{\"o}slichkeiten bei verschiedenen Windgeschwindigkeiten (1,4 m/s bis 8,4 m/s) bestimmt. Die dimensionslosen L{\"o}slichkeiten der verwendeten Gase deckten einen Bereich von 5 Gr{\"o}{\ss}enordnungen ab. Die Gaskonzentrationen wurden durch FTIR-Spektroskopie (Fourier Transform Infrared Spectroscopy) und mit einem PTR-MS (Proton Transfer Reaction - Mass Spectrometer) gemessen. Die Partitionierung des Transferwiderstandes von Gasen mittlerer L{\"o}slichkeit in einen luftseitigen und wasserseitigen Teil konnte nachgewiesen werden.},
	doi = {10.5281/zenodo.12327},
	url = {http://www.dpg-verhandlungen.de/year/2011/conference/dresden/part/up/session/1/contribution/29},
	author = {Christine Kr{\"a}uter and Kerstin E. Richter and Bernd J{\"a}hne and Evridiki Mesarchaki and Jonathan Williams}
}
@conference {kaster_11_comparative,
	title = {Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotations},
	booktitle = {LNCS},
	volume = {LNCS 6533},
	year = {2011},
	note = {1},
	pages = {74-85},
	publisher = {Springer, Heidelberg},
	organization = {Springer, Heidelberg},
	doi = {10.1007/978-3-642-18421-5_8},
	author = {F. O. Kaster and M.-A. Weber and Fred A. Hamprecht},
	editor = {Bjoern H. Menze and Bjoern H. Menze and Langs, G. and Criminisi, A. and Tu, Z.}
}
@conference {mischler2011,
	title = {Experimental setup for the investigation of bubble mediated gas exchange},
	booktitle = {Gas Transfer at Water Surfaces 2010},
	year = {2011},
	pages = {238--248},
	doi = {10.5281/zenodo.14953},
	url = {http://hdl.handle.net/2433/156156},
	author = {Wolfgang Mischler and Roland Rocholz and Bernd J{\"a}hne},
	editor = {S. Komori and W. R. McGilles and R. Kurose}
}
@conference {kiefhaber2011b,
	title = {Improved optical instrument for the measurement of water wave statistics in the field},
	booktitle = {Gas Transfer at Water Surfaces 2010},
	year = {2011},
	pages = {524--534},
	doi = {10.5281/zenodo.14958},
	url = {http://hdl.handle.net/2433/156156},
	author = {Daniel Kiefhaber and Roland Rocholz and Bernd J{\"a}hne},
	editor = {S. Komori and W. R. McGilles and R. Kurose}
}
@conference {richter2011a,
	title = {A laboratory study of the Schmidt number dependency of air-water gas transfer},
	booktitle = {Gas Transfer at Water Surfaces 2010},
	year = {2011},
	pages = {322--332},
	doi = {10.5281/zenodo.14955},
	url = {http://hdl.handle.net/2433/156156},
	author = {Kerstin E. Richter and Bernd J{\"a}hne},
	editor = {S. Komori and W. R. McGilles and R. Kurose}
}
@article {kaster_11_object-oriented,
	title = {An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements},
	journal = {Computer Science - Research and Development},
	volume = {26},
	year = {2011},
	note = {1},
	pages = {65-85},
	doi = {10.1007/s00450-010-0143-z},
	author = {F. O. Kaster and Merkel, B. and Nix, O. and Fred A. Hamprecht}
}
@conference {menze_11_on,
	title = {On oblique random forests},
	booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2011. Proceedings.},
	year = {2011},
	note = {1},
	pages = {453-469},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-23783-6_29},
	author = {Bjoern H. Menze and B. Michael Kelm and Splitthoff, N. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Rathke11,
	title = {Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography},
	booktitle = {MICCAI},
	volume = {6893},
	year = {2011},
	pages = {370--377},
	publisher = {Springer},
	organization = {Springer},
	author = {Rathke, Fabian and Schmidt, Stefan and Christoph Schn{\"o}rr},
	editor = {Fichtinger, Gabor and Martel, Anne L. and Peters, Terry M.}
}
@conference {Rathke11,
	title = {Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography},
	booktitle = {MICCAI},
	series = {Lecture Notes in Computer Science},
	volume = {6893},
	year = {2011},
	pages = {370{\textendash}377},
	publisher = {Springer},
	organization = {Springer},
	author = {Fabian Rathke and Stefan Schmidt and Christoph Schn{\"o}rr},
	editor = {Fichtinger, Gabor and Martel, Anne L. and Peters, Terry M.}
}
@conference {monroy:SCCH:2011,
	title = {Parametric Object Detection for Iconographic Analysis},
	booktitle = {Scientific Computing \& Cultural Heritage},
	year = {2011},
	url = {http://www.academia.edu/9439693/Parametric_Object_Detection_for_Iconographic_Analysis},
	author = {Monroy, A. and Thorben Kr{\"o}ger and Arnold, M. and Bj{\"o}rn Ommer}
}
@conference {monroy:ICIP:2011,
	title = {Reconstructing the Drawing Process of Reproductions from Medieval Images},
	booktitle = {Proceedings of the International Conference on Image Processing},
	year = {2011},
	pages = {2974--2977},
	publisher = {IEEE},
	organization = {IEEE},
	url = {https://hciweb.iwr.uni-heidelberg.de/compvis/research/manesse/},
	author = {Monroy, A. and Bernd Carque and Bj{\"o}rn Ommer}
}
@conference {yarlagadda:_:2011,
	title = {Top-down Analysis of Low-level Object Relatedness Leading to Semantic Understanding of Medieval Image Collections},
	booktitle = {Conference on Computer Vision and Image Analysis of Art II},
	volume = {7869},
	year = {2011},
	pages = {61--69},
	author = {Yarlagadda, P. and Monroy, A. and Bernd Carque and Bj{\"o}rn Ommer}
}
@conference {maier-hein2011,
	title = {Towards mobile augmented reality for on-patient visualization of medical images},
	booktitle = {Bildverarbeitung f{\"u}r die Medizin (2011)},
	year = {2011},
	pages = {389--393},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Despite considerable technical and algorithmic developments related to the fields of medical image acquisition and processing in the past decade, the devices used for visualization of medical images have undergone rather minor changes. As anatomical information is typically shown on monitors provided by a radiological work station, the physician has to mentally transfer internal structures shown on the screen to the patient. In this work, we present a new approach to on-patient visualization of 3D medical images, which combines the concept of augmented reality (AR) with an intuitive interaction scheme. The method requires mounting a Time-of-Flight (ToF) camera to a portable display (e.g., a tablet PC). During the visualization process, the pose of the camera and thus the viewing direction of the user is continuously determined with a surface matching algorithm. By moving the device along the body of the patient, the physician gets the impression of being able to look directly into the human body. The concept can be used for intervention planning, anatomy teaching and various other applications that require intuitive visualization of 3D data.},
	doi = {10.1007/978-3-642-19335-4_80},
	author = {Lena Maier-Hein and A. M. Franz and M. Fangerau and Schmidt, Mirko and Alexander Seitel and Sven Mersmann and T. Kilgus and A. Groch and K. Yung and Thiago R. dos Santos and Hans-Peter Meinzer},
	editor = {H. Handels and J. Ehrhardt and Hans-Peter Meinzer and T. Tolxdorff and T. M. Deserno}
}
@article {kelm_11_using,
	title = {Using Spatial Prior Knowledge in the Spectral Fitting of Magnetic Resonance Spectroscopic Images},
	journal = {NMR in Biomedicine},
	volume = {25(1)},
	year = {2011},
	note = {1},
	pages = {1-13},
	doi = {10.1002/nbm.1704},
	author = {B. Michael Kelm and F. O. Kaster and Henning, A. and M.-A. Weber and Bachert, P. and B{\"o}singer, P. and Fred A. Hamprecht and Bjoern H. Menze}
}
@conference {becker_11_variational,
	title = {Variational Recursive Joint Estimation of Dense Scene Structure and
Camera Motion from Monocular High Speed Traffic Sequences},
	booktitle = {2011 IEEE International Conference on Computer Vision ICCV},
	year = {2011},
	note = {1},
	pages = {1692-1699},
	doi = {10.1109/ICCV.2011.6126432},
	author = {Florian Becker and Frank Lenzen and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr},
	editor = {Metaxas, D. N. and Quan, L. and Van Gool, L. J. and Sanfeliu, A.}
}
@techreport {mischler2011a,
	title = {Vereinfachung der Massenbilanz im Blasentank f{\"u}r luftseitige Messung},
	year = {2011},
	institution = {Institut f{\"u}r Umweltphysik},
	author = {Wolfgang Mischler and Roland Rocholz and Weissbach, T.}
}
@conference {maier-hein2010a,
	title = {Accounting for an-iso-tro-pic noise in fine registration of time-of-flight range data with high-resolution surface data},
	booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010)},
	volume = {6361},
	year = {2010},
	pages = {251--258},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Time-of-Flight (ToF) sensors have become a considerable alternative to conventional surface acquisition techniques such as laser range scanning and stereo vision. Application of ToF cameras for the purpose of intra-operative registration requires matching of the noisy surfaces generated from ToF range data onto pre-interventionally acquired high-resolution surfaces. The contribution of this paper is two-fold: Firstly, we present a novel method for fine rigid registration of noisy ToF data with high-resolution surface meshes taking into account both, the noise characteristics of ToF cameras and the resolution of the target mesh. Secondly, we introduce an evaluation framework for assessing the performance of ToF registration methods based on physically realistic ToF range data generated from a virtual scence. According to experiments within the presented evaluation framework, the proposed method outperforms the standard ICP algorithm with respect to correspondence search and transformation computation, leading to a decrease in the target registration error (TRE) of more than 70\%.},
	doi = {10.1007/978-3-642-15705-9_31},
	author = {Lena Maier-Hein and Schmidt, Mirko and A. M. Franz and Thiago R. dos Santos and Alexander Seitel and Bernd J{\"a}hne and J. M. Fitzpatrick and Hans-Peter Meinzer}
}
@article {kreshuk_10_automated,
	title = {Automated detection and analysis of bimodal isotope peak distribution in H/D exchange mass spectrometry using HeXicon},
	journal = {International Journal of Mass Spectrometry},
	volume = {302},
	year = {2010},
	note = {1},
	pages = {125-131},
	doi = {10.1016/j.ijms.2010.08.025},
	author = {Anna Kreshuk and Stankiewicz, M. and Lou, X. and Kirchner, M. and Fred A. Hamprecht and Mayer, M. P.}
}
@article {lou_10_deuteration,
	title = {Deuteration Distribution Estimation with Improved Sequence Coverage for HX/MS Experiments},
	journal = {Bioinformatics},
	volume = {26(12)},
	year = {2010},
	note = {1},
	pages = {1535-1541},
	doi = {10.1093/bioinformatics/btq165},
	author = {Lou, X. and Kirchner, M. and B. Y. Renard and Ullrich K{\"o}the and Graf, C. and Lee, C. and Judith A. J. Steen and Steen, H. and Mayer, M. P. and Fred A. Hamprecht}
}
@mastersthesis {mischler2010,
	title = {Entwicklung eines Experiments zur Messung von Blasendichten und blaseninduziertem Gasaustausch},
	year = {2010},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	doi = {10.5281/zenodo.12742},
	author = {Wolfgang Mischler}
}
@conference {Lellmann2010a,
	title = {Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision},
	booktitle = {European Conference on Computer Vision (ECCV)},
	volume = {6312},
	year = {2010},
	pages = {494--505},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	author = {Lellmann, J. and Breitenreicher, D. and Christoph Schn{\"o}rr},
	editor = {Daniilidis, K. and Maragos, P. and Paragios, N.}
}
@conference {Lellmann2010a,
	title = {Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision},
	booktitle = {European Conference on Computer Vision (ECCV)},
	series = {LNCS},
	volume = {6312},
	year = {2010},
	pages = {494{\textendash}505},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	author = {Lellmann, J. and Breitenreicher, D. and Schn{\"o}rr, C.},
	editor = {Daniilidis, K. and Maragos, P. and Paragios, N.}
}
@conference {koethe_10_geometric,
	title = {Geometric Analysis of 3D Electron Microscopy Data},
	booktitle = {Proceedings of Workshop on Discrete Geometry and Mathematical Morphology (WADGMM)},
	year = {2010},
	note = {1},
	pages = {22-26},
	author = {Ullrich K{\"o}the and Bj{\"o}rn Andres and Thorben Kr{\"o}ger and Fred A. Hamprecht},
	editor = {Ullrich K{\"o}the and Montanvert, A. and Soille, P.}
}
@conference {kappes_10_mrf,
	title = {MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation},
	booktitle = {European Conference on Computer Vision (ECCV)},
	volume = {6313},
	year = {2010},
	note = {1},
	pages = {735--747},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-15558-1_53},
	author = {J{\"o}rg H. Kappes and Schmidt, S. and Christoph Schn{\"o}rr},
	editor = {Daniilidis, K. and Maragos, P. and Paragios, N.}
}
@conference {Kappes-ECCV2010,
	title = {MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation},
	booktitle = {European Conference on Computer Vision (ECCV)},
	volume = {6313},
	year = {2010},
	pages = {735--747},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	author = {J{\"o}rg H. Kappes and Schmidt, S. and Christoph Schn{\"o}rr},
	editor = {Daniilidis, K. and Maragos, P. and Paragios, N.}
}
@conference {Kappes-ECCV2010,
	title = {MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation},
	booktitle = {European Conference on Computer Vision (ECCV)},
	volume = {6313},
	year = {2010},
	pages = {735{\textendash}747},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	author = {Kappes, J. H. and Schmidt, S. and Schn{\"o}rr, C.},
	editor = {Daniilidis, K. and Maragos, P. and Paragios, N.}
}
@article {menze_11_multimodal,
	title = {Multimodal Medical Image Analysis: from Visualization to Disease Modeling},
	journal = {Zeitschrift f{\"u}r Med. Physik},
	volume = {1},
	year = {2010},
	pages = {1-2},
	doi = {10.1016/j.zemedi.2010.12.002},
	author = {Bjoern H. Menze and Fred A. Hamprecht}
}
@conference {kaster_10_object-oriented,
	title = {An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements},
	booktitle = {Bildverarbeitung f{\"u}r die Medizin 2010 -- Algorithmen, Systeme, Anwendungen},
	year = {2010},
	note = {1},
	pages = {97-101},
	publisher = {Springer},
	organization = {Springer},
	author = {F. O. Kaster and Kassemeyer, S. and Merkel, B. and Nix, O. and Fred A. Hamprecht},
	editor = {T. M. Deserno and H. Handels and T. Tolxdorff and Hans-Peter Meinzer}
}
@booklet {Chellappa2010,
	title = {Proceedings - 7th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2010},
	howpublished = {ACM International Conference Proceeding Series},
	year = {2010},
	publisher = {ACM},
	isbn = {9781450300605},
	author = {Chellappa, Rama. and Association for Computing Machinery.}
}
@conference {yarlagadda:ACCV:2010,
	title = {Recognition and Analysis of Objects in Medieval Images},
	booktitle = {Proceedins of the Aian Conference on Computer Vision, Workshop on e-Heritage},
	year = {2010},
	pages = {296--305},
	publisher = {Springer},
	organization = {Springer},
	author = {Yarlagadda, P. and Monroy, A. and Bernd Carque and Bj{\"o}rn Ommer}
}
@conference {Mansfield2010,
	title = {Scene carving: Scene consistent image retargeting},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6311 LNCS},
	number = {PART 1},
	year = {2010},
	pages = {143{\textendash}156},
	abstract = {Image retargeting algorithms often create visually disturbing distortion. We introduce the property of scene consistency, which is held by images which contain no object distortion and have the correct object depth ordering. We present two new image retargeting algorithms that preserve scene consistency. These algorithms make use of a user-provided relative depth map, which can be created easily using a simple GrabCut-style interface. Our algorithms generalize seam carving. We decompose the image retargeting procedure into (a) removing image content with minimal distortion and (b) re-arrangement of known objects within the scene to maximize their visibility. Our algorithms optimize objectives (a) and (b) jointly. However, they differ considerably in how they achieve this. We discuss this in detail and present examples illustrating the rationale of preserving scene consistency in retargeting. {\textcopyright} 2010 Springer-Verlag.},
	isbn = {3642155480},
	issn = {16113349},
	doi = {10.1007/978-3-642-15549-9_11},
	url = {www.fujifilm.com/products/3d/camera/finepix_},
	author = {Mansfield, Alex and Gehler, Peter and Van Gool, Luc and Carsten Rother}
}
@article {rathke2010structrank,
	title = {StructRank: A new approach for ligand-based virtual screening},
	journal = {J. Chem. Inf. Model.},
	volume = {51},
	year = {2010},
	pages = {83{\textendash}92},
	doi = {http://dx.doi.org/10.1021/ci100308f},
	author = {Rathke, Fabian and Hansen, Katja and Brefeld, Ulf and M{\"u}ller, Klaus-Robert}
}
@mastersthesis {meister_10_study,
	title = {A Study on Ground Truth Generation for Optical Flow},
	year = {2010},
	note = {1},
	school = {University of Heidelberg},
	author = {Stephan Meister}
}
@article {Heitz-et-al-09,
	title = {Variational fluid flow measurements from image sequences: synopsis and perspectives},
	journal = {Exp.~Fluids},
	volume = {48},
	number = {3},
	year = {2010},
	note = {DOI 10.1007/s00348-009-0778-3},
	pages = {369-393},
	author = {Heitz, D. and M{\'e}min, E. and Christoph Schn{\"o}rr}
}
@article {Heitz-et-al-09,
	title = {Variational fluid flow measurements from image sequences: synopsis and perspectives},
	journal = {Exp. Fluids},
	volume = {48},
	number = {3},
	year = {2010},
	note = {DOI 10.1007/s00348-009-0778-3},
	pages = {369-393},
	author = {Heitz, D. and M{\'e}min, E. and Schn{\"o}rr, C.}
}
@conference {yarlagadda:ECCV:2010,
	title = {Voting by Grouping Dependent Parts},
	booktitle = {Proceedings of the European Conference on Computer Vision},
	volume = {6315},
	year = {2010},
	pages = {197--210},
	publisher = {Springer},
	organization = {Springer},
	author = {Yarlagadda, P. and Monroy, A. and Bj{\"o}rn Ommer}
}
@article {hayn_09_analysing,
	title = {Analysing spatio-temporal patterns of the global NO<SUB>2</SUB>-distribution retrieved frome GOME satellite observations using a generalized additive model},
	journal = {Atmospheric Chemistry and Physics},
	volume = {9},
	number = {17},
	year = {2009},
	note = {1},
	pages = {9367-9398},
	doi = {10.5194/acp-9-6459-2009},
	author = {Hayn, M. and S. Beirle and Fred A. Hamprecht and Ulrich Platt and Bjoern H. Menze and T. Wagner}
}
@article {hansen2009bias,
	title = {Bias-correction of regression models: a case study on hERG inhibition},
	journal = {J. Chem. Inf. Model.},
	volume = {49},
	year = {2009},
	pages = {1486{\textendash}1496},
	doi = {http://dx.doi.org/10.1021/ci9000794},
	author = {Hansen, Katja and Rathke, Fabian and Schroeter, Timon and Rast, Georg and Fox, Thomas and Kriegl, Jan M and Mika, Sebastian}
}
@article {ozlu_09_binding,
	title = {Binding partner switching on microtubules and aurora-B in the mitosis to cytokinesis transition},
	journal = {Molecular \& Cellular Proteomics},
	year = {2009},
	note = {1},
	doi = {10.1074/mcp.M900308-MCP200},
	author = {Ozlu, N. and Monigatti, F. and B. Y. Renard and Field, C. M. and Steen, H. and Mitchison, T. J. and Judith Jebanthirajah Steen}
}
@article {menze_09_comparison,
	title = {A Comparison of Random Forest and its Gini Importance with Standard Chemometric Methods for the Feature Selection and Classification of Spectral Data},
	journal = {BMC Bioinformatics},
	volume = {10:213},
	year = {2009},
	note = {1},
	doi = {10.1186/1471-2105-10-213},
	author = {Bjoern H. Menze and B. Michael Kelm and Masuch, R. and Himmelreich, U. and Bachert, P. and Petrich, W. and Fred A. Hamprecht}
}
@conference {lellmann_09_convex2,
	title = {Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation},
	booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM 2009)},
	volume = {5567},
	year = {2009},
	note = {1},
	pages = {150-162},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-02256-2_13},
	author = {Lellmann, J. and J{\"o}rg H. Kappes and Yuan, J. and Florian Becker and Christoph Schn{\"o}rr and M{\'o}rken, K. and Lysaker, M.},
	editor = {Tai, X.-C. and Lie, K.-A.}
}
@conference {Lellmann-et-al-09a,
	title = {Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation},
	booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM 2009)},
	volume = {5567},
	year = {2009},
	pages = {150-162},
	publisher = {Springer},
	organization = {Springer},
	author = {Lellmann, J. and J{\"o}rg H. Kappes and Yuan, J. and Florian Becker and Christoph Schn{\"o}rr},
	editor = {Tai, X.-C. and M{\'o}rken, K. and Lie, K.-A. and Lysaker, M.}
}
@article {kelm_09_estimating,
	title = {Estimating Kinetic Parameter Maps from Dynamic Contrast-Enhanced MRI using Spatial Prior Knowledge},
	journal = {IEEE Transaction on Medical Imaging},
	volume = {28:10},
	year = {2009},
	note = {1},
	pages = {1534-1547},
	doi = {10.1109/TMI.2009.2019957},
	author = {B. Michael Kelm and Bjoern H. Menze and Nix, O. and C. M. Zechmann and Fred A. Hamprecht}
}
@conference {ommer:ICCV:2009,
	title = {Multi-scale Object Detection by Clustering Lines},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	year = {2009},
	pages = {484--491},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Bj{\"o}rn Ommer and Malik, J.}
}
@mastersthesis {mbock2009,
	title = {A Novel Algorithm for Motion Estimation with Explicit Consideration of Perturbations},
	year = {2009},
	school = {University of Heidelberg},
	author = {Mbock, K.}
}
@conference {mischler2009,
	title = {Optical method for measuring size-distribution and lifetime of bubbles},
	booktitle = {Poster abstracts SOLAS Open Science Conference, Barcelona, 16--19 Sep. 2009},
	year = {2009},
	abstract = {A high resolution imaging method for measuring the size distribution of bubbles entrained by water jets between 10-1000 um in diameter is presented. The bubble clouds are similar to those produced by breaking water waves. The goal of these measurements is the validation of models for bubble mediated gas transfer. A Depth from Focus technique is used to determine the 3D position and the size of the bubbles at the same time. This yields the size-distribution and its dependence on depth. Additionally the velocity of the bubbles is estimated using repeatedly exposed images. To evaluate the lifetime of bubbles a high-speed camera is used to count the bubbles crossing the surface. The measuring principle, image analysis and first results are presented.},
	author = {Wolfgang Mischler and Roland Rocholz and Bernd J{\"a}hne}
}
@conference {meine_09_pixel,
	title = {Pixel Approximation Errors in Common Watershed Algorithms},
	booktitle = {Discrete Geometry for Computer Imagery},
	volume = {5810},
	year = {2009},
	note = {1},
	pages = {193-202},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-04397-0},
	author = {Meine, H. and Ullrich K{\"o}the and Stelldinger, P.}
}
@conference {meine_09_topological,
	title = {Pixel Approximation Errors in Common Watershed Algorithms},
	booktitle = {Discrete Geometry for Computer Imagery},
	volume = {5810},
	year = {2009},
	pages = {193-202},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-04397-0},
	author = {Meine, H. and Ullrich K{\"o}the and Stelldinger, P.}
}
@article {goerlitz_09_processing,
	title = {Processing Spectral Data},
	journal = {Surface and Interface Analysis},
	volume = {41},
	year = {2009},
	note = {1},
	pages = {636-644},
	doi = {10.1002/sia.3066},
	author = {G{\"o}rlitz, L. and Bjoern H. Menze and B. Michael Kelm and Fred A. Hamprecht}
}
@article {ommer:IJCV:2009,
	title = {Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera},
	journal = {International Journal of Computer Vision},
	volume = {83},
	number = {1},
	year = {2009},
	pages = {57--71},
	publisher = {Springer},
	author = {Bj{\"o}rn Ommer and Mader, T. and J. M. Buhmann}
}
@conference {Yuan-et-al-09a,
	title = {Total-Variation Based Piecewise Affine Regularization},
	booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM 2009)},
	volume = {5567},
	year = {2009},
	pages = {552-564},
	publisher = {Springer},
	organization = {Springer},
	author = {Yuan, Jing. and Christoph Schn{\"o}rr and Steidl, Gabriele},
	editor = {Tai, X.-C. and M{\'o}rken, K. and Lie, K.-A. and Lysaker, M.}
}
@conference {Yuan-et-al-09a,
	title = {Total-Variation Based Piecewise Affine Regularization},
	booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM 2009)},
	series = {LNCS},
	volume = {5567},
	year = {2009},
	pages = {552-564},
	publisher = {Springer},
	organization = {Springer},
	author = {Yuan, Jing. and Christoph Schn{\"o}rr and Steidl, Gabriele},
	editor = {Tai, X.-C. and M{\'o}rken, K. and Lysaker, M. and Lie, K.-A.}
}
@conference {yarlagadda:SCCH:2009,
	title = {Towards a Computer-based Understanding of Medieval Images},
	booktitle = {Scientific Computing \& Cultural Heritage},
	year = {2009},
	pages = {89--97},
	publisher = {Springer},
	organization = {Springer},
	url = {http://link.springer.com/chapter/10.1007\%2F978-3-642-28021-4_10$\#$page-1},
	author = {Yarlagadda, P. and Monroy, A. and Bernd Carque and Bj{\"o}rn Ommer}
}
@article {renard_09_when,
	title = {When Less Can Yield More - Computational Preprocessing of MS/MS Spectra for Peptide Identification Preprocessing},
	journal = {Proteomics},
	volume = {9},
	year = {2009},
	note = {1},
	pages = {4978-4984},
	doi = {10.1002/pmic.200900326},
	author = {B. Y. Renard and Kirchner, M. and Monigatti, F. and Ivanov, A. R. and Rappsilber, J. and Winter, D. and Judith A. J. Steen and Fred A. Hamprecht and Steen, H.}
}
@conference {Gehler2008,
	title = {Bayesian color constancy revisited},
	booktitle = {26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR},
	year = {2008},
	abstract = {Computational color constancy is the task of estimating the true reflectances of visible surfaces in an image. In this paper we follow a line of research that assumes uniform illumination of a scene, and that the principal step in estimating reflectances is the estimation of the scene illuminant. We review recent approaches to illuminant estimation, firstly those based on formulae for normalisation of the reflectance distribution in an image - so-called grey-world algorithms, and those based on a Bayesian formulation of image formation. In evaluating these previous approaches we introduce a new tool in the form of a database of 568 high-quality, indoor and outdoor images, accurately labelled with illuminant, and preserved in their raw form, free of correction or normalisation. This has enabled us to establish several properties experimentally. Firstly automatic selection of grey-world algorithms according to image properties is not nearly so effective as has been thought. Secondly, it is shown that Bayesian illuminant estimation is significantly improved by the improved accuracy of priors for illuminant and reflectance that are obtained from the new dataset. {\textcopyright}2008 IEEE.},
	isbn = {9781424422432},
	doi = {10.1109/CVPR.2008.4587765},
	author = {Gehler, Peter Vincent and Carsten Rother and Blake, Andrew and Minka, Tom and Sharp, Toby}
}
@incollection {acker2008,
	title = {Inverse Problems and Parameter Identification in Image Processing},
	year = {2008},
	pages = {111--151},
	publisher = {Springer},
	abstract = {Many problems in imaging are actually inverse problems. One reason for this is that conditions and parameters of the physical processes underlying the actual image acquisition are usually not known. Examples for this are the inhomogeneities of the magnetic field in magnetic resonance imaging (MRI) leading to nonlinear deformations of the anatomic structures in the recorded images, material parameters in geological structures as unknown parameters for the simulation of seismic wave propagation with sparse measurement on the surface, or temporal changes in movie sequences given by intensity changes or moving image edges and resulting from deformation, growth and transport processes with unknown fluxes. The underlying physics is mathematically described in terms of variational problems or evolution processes. Hence, solutions of the forward problem are naturally described by partial differential equations. These forward models are reflected by the corresponding inverse problems as well. Beyond these concrete, direct modeling links to continuum mechanics abstract concepts from physical modeling are successfully picked up to solve general perceptual problems in imaging. Examples are visually intuitive methods to blend between images showing multiscale structures at different resolution or methods for the analysis of flow fields.},
	doi = {10.1007/978-3-540-75632-3_4},
	author = {Jens F. Acker and Benjamin Berkels and Kristian Bredies and Mamadou S. Diallo and Marc Droske and Christoph S. Garbe and Matthias Holschneider and Jaroslav Hron and Claudia Kondermann and Michail Kulesh and Peter Maass and Nadine Olischl{\"a}ger and Heinz-Otto Peitgen},
	editor = {R. Dahlhaus and J. Kurths and Timmer, J. and Peter Maass}
}
@article {menze_08_mimicking,
	title = {Mimicking the human expert: pattern recognition for an automated assessment of data quality in MRSI},
	journal = {Magnetic Resonance in Medicine},
	volume = {59},
	year = {2008},
	note = {1},
	pages = {1457-1466},
	doi = {10.1002/mrm.21519},
	author = {Bjoern H. Menze and B. Michael Kelm and M.-A. Weber and Bachert, P. and Fred A. Hamprecht}
}
@incollection {garbe_08_nonlinear,
	title = {Nonlinear Analysis of Multi-Dimensional Signals},
	year = {2008},
	note = {1},
	pages = {231-288},
	publisher = {Springer},
	doi = {10.1007/978-3-540-75632-3_7},
	author = {Christoph S. Garbe and Kai Krajsek and Pavel Pavlov and Bj{\"o}rn Andres and Matthias M{\"u}hlich and Ingo Stuke and Cicero Mota and Martin B{\"o}hme and Martin Haker and Schuchert, T. and Hanno Scharr and Til Aach and Erhardt Barth},
	editor = {R. Dahlhaus and J. Kurths and Timmer, J. and Peter Maass}
}
@incollection {garbe2008,
	title = {Nonlinear analysis of multi-dimensional signals: local adaptive estimation of complex motion and orientation patterns},
	year = {2008},
	pages = {231-288},
	publisher = {Springer},
	abstract = {We consider the general task of accurately detecting and quantifying orientations in n-dimensional signals s. The main emphasis will be placed on the estimation of motion, which can be thought of as orientation in spatiotemporal signals. Associated problems such as the optimization of matched kernels for deriving isotropic and highly accurate gradients from the signals, optimal integration of local models, and local model selection will also be addressed.},
	doi = {10.1007/978-3-540-75632-3_7},
	author = {Christoph S. Garbe and Kai Krajsek and Pavel Pavlov and Bj{\"o}rn Andres and Matthias M{\"u}hlich and Ingo Stuke and Cicero Mota and Martin B{\"o}hme and Martin Haker and Tobias Schucher and Hanno Scharr and Til Aach and Erhardt Barth},
	editor = {R. Dahlhaus and J. Kurths and Timmer, J. and Peter Maass}
}
@article {Munder-et-al-08,
	title = {Pedestrian Detection and Tracking Using a Mixture of View-Based Shape-Texture Models},
	journal = {IEEE Trans. Intell. Transp. Systems},
	volume = {9},
	year = {2008},
	pages = {333-343},
	author = {Munder, S. and Schn{\"o}rr, C. and Gavrila, D. M.}
}
@article {koenig_08_robust,
	title = {Robust Prediction of the MASCOT Score for an Improved Quality Assessment in Mass Spectrometric Proteomics},
	journal = {Journal of Proteome Research},
	volume = {7},
	number = {9},
	year = {2008},
	note = {1},
	pages = {3708-3717},
	doi = {10.1021/pr700859x},
	author = {K{\"o}nig, T. and Bjoern H. Menze and Kirchner, M. and Monigatti, F. and Kenneth C. Parker and Patterson, T. and Judith Jebanthirajah Steen and Fred A. Hamprecht and Steen, H.}
}
@conference {kondermann2008a,
	title = {A statistical confidence measure for optical flows},
	booktitle = {Proceedings of the ECCV},
	volume = {5304},
	year = {2008},
	pages = {290--301},
	abstract = {Confidence measures are crucial to the interpretation of any optical flow measurement. Even though numerous methods for estimating optical flow have been proposed over the last three decades, a sound, universal, and statistically motivated confidence measure for optical flow measurements is still missing. We aim at filling this gap with this contribution, where such a confidence measure is derived, using statistical test theory and measurable statistics of flow fields from the regarded domain. The new confidence measure is computed from merely the results of the optical flow estimator and hence can be applied to any optical flow estimation method, covering the range from local parametric to global variational approaches. Experimental results using state-of-the-art optical flow estimators and various test sequences demonstrate the superiority of the proposed technique compared to existing confidence measures.},
	doi = {10.1007/978-3-540-88690-7_22},
	author = {Claudia Kondermann and Mester, R. and Christoph S. Garbe}
}
@article {meine_08_topological,
	title = {A Topological Sampling Theorem for Robust Boundary Reconstruction
and Image Segmentation},
	journal = {Discrete Applied Mathematics},
	volume = {157},
	number = {3},
	year = {2008},
	pages = {524-541},
	doi = {10.1016/j.dam.2008.05.031},
	author = {Meine, H. and Ullrich K{\"o}the and Stelldinger, P.}
}
@article {kelm_07_automated,
	title = {Automated Estimation of Tumor Probability in Prostate MRSI: Pattern Recognition vs. Quantification},
	journal = {Magnetic Resonance in Medicine},
	volume = {57},
	number = {1},
	year = {2007},
	note = {1},
	pages = {150-159},
	doi = {10.1002/mrm.21112},
	author = {B. Michael Kelm and Bjoern H. Menze and C. M. Zechmann and Baudendistel, K. T. and Fred A. Hamprecht}
}
@conference {menze_07_classification,
	title = {Classification of multispectral ASTER imagery in the archaeological survey for settlement sites of the Near East},
	booktitle = {Proc 10th International Symposium on Physical Measurements and Signature in Remote Sensing (ISPMRS 07), Davos, Switzerland},
	year = {2007},
	note = {1},
	publisher = {International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
	organization = {International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
	author = {Bjoern H. Menze and Ur, J. A.}
}
@book {jaehne2007a,
	title = {Complex Motion, Proceedings of the 1st Workshop, G{\"u}nzburg, October 2004},
	volume = {3417},
	year = {2007},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-540-69866-1},
	author = {Bernd J{\"a}hne},
	editor = {Mester, R. and Hanno Scharr and Erhardt Barth}
}
@article {Yuan-et-al-jmiv-06,
	title = {Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation},
	journal = {J.~Math.~Imag.~Vision},
	volume = {28},
	year = {2007},
	pages = {67-80},
	author = {Yuan, J. and Christoph Schn{\"o}rr and M{\'e}min, E.}
}
@conference {menze_07_from,
	title = {From eigenspots to fisherspots -- latent spaces in the nonlinear detection of spot patterns in a highly variable background},
	booktitle = {Advances in data analysis},
	volume = {33},
	year = {2007},
	note = {1},
	pages = {255-262},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-540-70981-7_29},
	author = {Bjoern H. Menze and B. Michael Kelm and Fred A. Hamprecht},
	editor = {Lenz, H.-J. and Decker, R.}
}
@article {menze_07_multivariate,
	title = {Multivariate feature selection and hierarchical classification for infrared spectroscopy: serum-based detection of bovine spongiform encephalopathy},
	journal = {Analytical and Bioanalytical Chemistry},
	volume = {387},
	number = {5},
	year = {2007},
	note = {1},
	pages = {1801-1807},
	doi = {10.1007/s00216-006-1070-5},
	author = {Bjoern H. Menze and Petrich, W. and Fred A. Hamprecht}
}
@phdthesis {menze_07_mustererkennung,
	title = {Pattern Recognition in the Quantitative Analysis of Vector-Valued Image Data: Diagnostic Systems and Applications},
	year = {2007},
	publisher = {University of Heidelberg},
	author = {Bjoern H. Menze}
}
@conference {goerlitz_07_semi,
	title = {Semi-Supervised Tumor Detection in MRSI With Discriminative Random Fields},
	booktitle = {Pattern Recognition},
	volume = {4713},
	year = {2007},
	note = {1},
	pages = {224-233},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-540-74936-3_23},
	author = {G{\"o}rlitz, L. and Bjoern H. Menze and M.-A. Weber and B. Michael Kelm},
	editor = {Fred A. Hamprecht and Fred A. Hamprecht and Christoph Schn{\"o}rr and Bernd J{\"a}hne}
}
@conference {menze_07_virtual,
	title = {Virtual Survey on North Mesopotamian Tell Sites by Means of Satellite Remote Sensing},
	booktitle = {Broadening Horizons: Multidisciplinary Approaches to Landscape Study},
	year = {2007},
	note = {1},
	pages = {5-29},
	publisher = {Cambridge Scholars Publishing},
	organization = {Cambridge Scholars Publishing},
	author = {Bjoern H. Menze and Muehl, S. and Sherratt, A. G.},
	editor = {Ooghe, B. et al.}
}
@conference {kelm_06_bayesian,
	title = {Bayesian Estimation of Smooth Parameter Maps for Dynamic Contrast-Enhanced MR Images with Block-ICM},
	booktitle = {Proc Computer Vision and Pattern Recognition Workshop (Mathematical Methods in Biomedical Image Analysis)},
	year = {2006},
	note = {1},
	pages = {96-103},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	doi = {10.1109/CVPRW.2006.41},
	author = {B. Michael Kelm and M{\"u}ller, N. and Bjoern H. Menze and Fred A. Hamprecht},
	editor = {Schmid, C. and Soatto, S. and Tomasi, C.}
}
@conference {kelm_06_claret,
	title = {CLARET: a tool for fully automated evaluation of MRSI with pattern recognition methods.},
	booktitle = {Bildverarbeitung f{\"u}r die Medizin 2006 - Algorithmen, Systeme, Anwendungen},
	year = {2006},
	note = {1},
	pages = {51-55},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/3-540-32137-3_7},
	url = {http://www.efmi-wg-mip.net/service/bvm2006},
	author = {B. Michael Kelm and Bjoern H. Menze and Neff, T. and C. M. Zechmann and Fred A. Hamprecht},
	editor = {H. Handels and G. Bebis and et al.}
}
@inbook {carlsohn_06_spectral,
	title = {Color image processing},
	volume = {7(17)},
	year = {2006},
	pages = {393-419},
	publisher = {CRC Press},
	organization = {CRC Press},
	chapter = {Spectral Imaging and Applications},
	author = {Carlsohn, M. F. and Bjoern H. Menze and B. Michael Kelm and Fred A. Hamprecht and Kercek, A. and Leitner, R. and Polder, G.},
	editor = {Lukac, R. and Plataniotis, K.N.}
}
@conference {kelm_06_combining,
	title = {Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning.},
	booktitle = {ICPR 2006},
	volume = {2},
	year = {2006},
	note = {1},
	pages = {828-832},
	doi = {10.1109/ICPR.2006.384},
	author = {B. Michael Kelm and Pal, C. and McCallum, A.}
}
@conference {Rother2006,
	title = {Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	volume = {1},
	year = {2006},
	pages = {994{\textendash}1000},
	abstract = {We introduce the term cosegmentation which denotes the task of segmenting simultaneously the common parts of an image pair. A generative model for cosegmentation is presented. Inference in the model leads to minimizing an energy with an MRF term encoding spatial coherency and a global constraint which attempts to match the appearance histograms of the common parts. This energy has not been proposed previously and its optimization is challenging and NP-hard. For this problem a novel optimization scheme which we call trust region graph cuts is presented. We demonstrate that this framework has the potential to improve a wide range of research: Object driven image retrieval, video tracking and segmentation, and interactive image editing. The power of the framework lies in its generality, the common part can be a rigid/non-rigid object (or scene), observed from different viewpoints or even similar objects of the same class. {\textcopyright} 2006 IEEE.},
	isbn = {0769525970},
	issn = {10636919},
	doi = {10.1109/CVPR.2006.91},
	url = {http://research.microsoft.com/vision/cambridge/},
	author = {Carsten Rother and Kolmogorov, Vladimir and Minka, Tom and Blake, Andrew}
}
@article {menze_06_detection,
	title = {Detection of ancient settlement mounds - Archaeological survey based on the SRTM terrain model},
	journal = {Photgrammetric Engineering \& Remote Sensing},
	volume = {3},
	year = {2006},
	note = {1},
	pages = {321-327},
	author = {Bjoern H. Menze and Ur, J. A. and Sherratt, A. G.}
}
@conference {ommer:CVPR:2006,
	title = {Learning Top-Down Grouping of Compositional Hierarchies for Recognition},
	booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Perceptual Organization in Computer Vision},
	year = {2006},
	pages = {194--194},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Bj{\"o}rn Ommer and Sauter, M. and M., Buhmann J.}
}
@conference {menze_06_machine-based,
	title = {Machine-based rejection of low quality spectra and estimation of brain tumor probabilities from magnetic resonance spectroscopic images},
	booktitle = {Bildverarbeitung f{\"u}r die Medizin},
	year = {2006},
	note = {1},
	pages = {31-36},
	doi = {10.1007/3-540-32137-3_7},
	author = {Bjoern H. Menze and B. Michael Kelm and Heck, D. and M. P. Lichy and Fred A. Hamprecht},
	editor = {H. Handels and G. Bebis and et al.}
}
@article {menze_06_optimal,
	title = {Optimal Classification of Long Echo Time in vivo Magnetic Resonance Spectra in the Detection of Recurrent Brain Tumor},
	journal = {NMR in Biomedicine},
	volume = {19},
	number = {5},
	year = {2006},
	note = {1},
	pages = {599-609},
	doi = {10.1002/nbm.1041},
	author = {Bjoern H. Menze and M. P. Lichy and Bachert, P. and B. Michael Kelm and Schlemmer, H. P. and Fred A. Hamprecht}
}
@article {sieg_06_qcar,
	title = {A QCAR-approach to materials modelling},
	journal = {Journal of Molecular Modeling},
	volume = {12},
	year = {2006},
	note = {1},
	pages = {611-619},
	doi = {10.1007/s00894-005-0068-9},
	author = {Sieg, S. and Stutz, B. and Schmidt, T. and Fred A. Hamprecht and Maier, W. F.}
}
@conference {kelm_05_automatische,
	title = {Automatische Lokalisation von Tumoren in 1H-NMR-spektroskopischen in vivo Aufnahmen},
	booktitle = {VDI-Berichte},
	volume = {1883},
	year = {2005},
	note = {1},
	pages = {457-466},
	author = {B. Michael Kelm and Bjoern H. Menze and Fred A. Hamprecht}
}
@conference {Yuan-et-al-05a,
	title = {Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation},
	booktitle = {Scale-Space 2005},
	series = {lncs},
	volume = {3459},
	year = {2005},
	pages = {267{\textendash}278},
	publisher = {Springer},
	organization = {Springer},
	author = {Yuan, J. and Ruhnau, P. and M{\'e}min, E. and Schn{\"o}rr, C.}
}
@conference {menze_05_tell,
	title = {Tell Spotting - Surveying Near Eastern Settlement Mounds from Space},
	booktitle = {Proceedings of the XXth CIPA International Symposium 2005, Torino, Italy},
	year = {2005},
	note = {1},
	pages = {217-223},
	author = {Bjoern H. Menze and Ur, J. A. and Sherratt, A. G.}
}
@article {frew2004,
	title = {Air-sea gas transfer: Its dependence on wind stress, small-scale roughness, and surface films},
	journal = {J. Geophys. Res.},
	volume = {109},
	year = {2004},
	pages = {C08S17},
	doi = {10.1029/2003JC002131},
	author = {Nelson M. Frew and E. J. Bock and Uwe Schimpf and T. Hara and Horst Hau{\ss}ecker and J. B. Edson and W. R. McGilles and R. K. Nelson and S. P. McKenna and B. M. Uz and Bernd J{\"a}hne}
}
@conference {Giebel-et-al-04,
	title = {A Bayesian Framework for Multi-cue 3D Object Tracking},
	booktitle = {Computer Vision {\textendash} ECCV 2004},
	series = {lncs},
	volume = {3024},
	year = {2004},
	pages = {241-252},
	publisher = {Springer},
	organization = {Springer},
	author = {Giebel, J. and Gavrila, D. M. and Schn{\"o}rr, C.},
	editor = {Pajdla, T. and Matas, J.}
}
@conference {menze_04_classification,
	title = {Classification of in vivo magnetic resonance spectra},
	booktitle = {Classification in ubiquitous challenge: Proceedings of the GfKl 2004},
	year = {2004},
	pages = {362-369},
	publisher = {Springer},
	organization = {Springer},
	author = {Bjoern H. Menze and Wormit, M. and Bachert, P. and M. P. Lichy and Schlemmer, H.-P. and Fred A. Hamprecht}
}
@mastersthesis {menze_04_klassifikation,
	title = {Klassifikation von Magnetresonanzspektren},
	year = {2004},
	school = {University of Heidelberg},
	author = {Bjoern H. Menze}
}
@conference {Cremers-et-al-04,
	title = {Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation},
	booktitle = {Computer Vision {\textendash} ECCV 2004},
	series = {lncs},
	volume = {3024},
	year = {2004},
	pages = {74-86},
	publisher = {Springer},
	organization = {Springer},
	author = {Daniel Cremers and Sochen, Nir and Christoph Schn{\"o}rr},
	editor = {Pajdla, T. and Matas, J.}
}
@conference {Kohlberger-et-al-04a,
	title = {Parallel Variational Motion Estimation by Domain Decomposition and Cluster Computing},
	booktitle = {Computer Vision {\textendash} ECCV 2004},
	series = {lncs},
	volume = {3024},
	year = {2004},
	pages = {205-216},
	publisher = {Springer},
	organization = {Springer},
	author = {Kohlberger, T. and Schn{\"o}rr, C. and Bruhn, A. and Weickert, J.},
	editor = {Pajdla, T. and Matas, J.}
}
@incollection {scholes2003,
	title = {Biosphere-Atmosphere Interactions},
	year = {2003},
	pages = {19--71},
	publisher = {Springer},
	chapter = {2},
	doi = {10.1007/978-3-642-18984-5_2},
	author = {Mary C. Scholes and Patricia A. Matrai and Meinrat O. Andreae and Keith A. Smith and Martin R. Manning and Paulo Artaxo and Leonard A. Barrie and Timothy S. Bates and James H. Butler and Paolo Ciccioli and Stanislaw A. Cieslik and Robert J. Delmas and Frank J. Dentener},
	editor = {Guy P. Brasseur and Ronald G. Prinn and Alexander A. P. Pszenny}
}
@conference {Kohlberger-et-al-03c,
	title = {Domain Decomposition for Parallel Variational Optical Flow Computation},
	booktitle = {Pattern Recognition, Proc. 25th DAGM Symposium},
	series = {LNCS},
	volume = {2781},
	year = {2003},
	pages = {196{\textendash}203},
	publisher = {Springer},
	organization = {Springer},
	author = {Kohlberger, T. and Schn{\"o}rr, C. and Bruhn, A. and Weickert, J.},
	editor = {Michaelis, B. and Krell, G.}
}
@conference {jaehne2003a,
	title = {Image sequence analysis in environmental and live sciences},
	booktitle = {Proceedings of the 25th DAGM Symposium on Pattern Recognition},
	volume = {2781},
	year = {2003},
	note = {invited},
	pages = {608--617},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Image sequence processing techniques are essential to study dynamical processes such as exchange, growth, and transport processes. In this survey paper, a generalized framework for the estimation of the parameters of dynamic processes including motion fields is presented. Some examples from environmental and live sciences illustrate how this framework helped to tackles some key questions that could not be solved without taking and analyzing image sequences.},
	doi = {10.1007/978-3-540-45243-0_77},
	author = {Bernd J{\"a}hne},
	editor = {B. Michaelis and G. Krell}
}
@conference {Kohlberger-et-al-03b,
	title = {Variational Dense Motion Estimation Using the Helmholtz Decomposition},
	booktitle = {Scale Space Methods in Computer Vision},
	series = {LNCS},
	volume = {2695},
	year = {2003},
	pages = {432{\textendash}448},
	publisher = {Springer},
	organization = {Springer},
	author = {Kohlberger, T. and M{\'e}min, E. and Schn{\"o}rr, C.},
	editor = {Griffin, L.D. and Lillholm, M.}
}
@conference {garbe2002b,
	title = {Measuring important parameters for air-sea heat exchange},
	booktitle = {ThermoSense},
	volume = {4710},
	year = {2002},
	pages = {171--182},
	publisher = {SPIE},
	organization = {SPIE},
	doi = {10.1117/12.459564},
	author = {Christoph S. Garbe and Uwe Schimpf and Bernd J{\"a}hne},
	editor = {Maldague, X. P. and Rozlosnik, A. E.}
}
@conference {bock2002,
	title = {Overview of the CoOP experiments: physical and chemical measurements parameterizing air-sea gas transfer},
	booktitle = {Gas Transfer at Water Surfaces},
	volume = {127},
	year = {2002},
	pages = {39--44},
	publisher = {American Geophysical Union},
	organization = {American Geophysical Union},
	doi = {10.1029/GM127p0039},
	author = {E. J. Bock and J. B. Edson and Nelson M. Frew and T. Hara and Horst Hau{\ss}ecker and Bernd J{\"a}hne and W. R. McGilles and S. P. McKenna and R. K. Nelson and Uwe Schimpf and B. M. Uz},
	editor = {M. A. Donelan and W. M. Drennan and R. Wanninkhof and E. S. Saltzman}
}
@conference {hara2002a,
	title = {Surface wave observations during CoOP experiments and their relations to air-sea gas transfer},
	booktitle = {Gas Transfer at Water Surfaces},
	volume = {127},
	year = {2002},
	pages = {45--49},
	publisher = {American Geophysical Union},
	organization = {American Geophysical Union},
	author = {T. Hara and B. M. Uz and H. Wei and J. B. Edson and Nelson M. Frew and W. R. McGilles and S. P. McKenna and E. J. Bock and Horst Hau{\ss}ecker and Uwe Schimpf},
	editor = {M. A. Donelan and W. M. Drennan and R. Wanninkhof and E. S. Saltzman}
}
@conference {garbe2002d,
	title = {Thermographic measurements on plant leaves},
	booktitle = {ThermoSense},
	volume = {4710},
	year = {2002},
	pages = {407--416},
	publisher = {SPIE},
	organization = {SPIE},
	abstract = {An important process of plant physiology is the transpiration of plant leaves. It is actively controlled by pores (stomata) in the leaf and the governing feature for vital factors such as gas exchange and water transport affixed to which is the nutrient transport from the root to the shoot. Because of its importance, the transpiration and water transport in leaves have been extensively studied. However, current measurement techniques provide poor spatial and temporal resolution. With the use of one single low-NETD infrared camera important parameter of plant physiology such as transpiration rates, heat capacity per unit area of the leaf and the water flow velocity can be measured to high temporal and special resolution by techniques presented in this paper. The latent heat flux of a plant, which is directly proportional to the transpiration rate, can be measured with passive thermography. Here use is made of the linear relationship between the temperature difference between a non transpiring reference body and the transpiring leaf and the latent heat flux. From active thermography the heat capacity per unit area of the leaf can be measured. This method is termed active, because the response of the leaf temperature to an imposed energy flux is measured. Through the use of digital image processing techniques simultaneous measurements of the velocity field and temporal change of heated water parcels traveling through the leaf can be estimated from thermal image sequences.},
	doi = {10.1117/12.459590},
	author = {Christoph S. Garbe and Ulrich Schurr and Bernd J{\"a}hne},
	editor = {Maldague, X. P. and Rozlosnik, A. E.}
}
@conference {klinke2001,
	title = {Wavenumber Spectra of Short Wind Waves: Laboratory Measurements and Interpretation},
	booktitle = {IGARSS {\textquoteright}01, Geoscience and Remote Sensing Symposium, Sydney, NSW, Australia},
	volume = {2},
	year = {2001},
	pages = {965-967},
	abstract = {Short wind-generated capillary-gravity waves were measured with a refraction-based optical technique in different wind/wave tanks. Directional wave number spectra were determined from the wave slope images for a wide range of wind speeds and fetches for the different geometries of the laboratory facilities. The shape of the wavenumber spectra and their dependence on die friction velocity and fetch were analyzed. We found that the parasitic capillaries dominate the capillary range of the spectra. The interpretation of the results is given by a physical model of the short wind-wave spectrum that takes the generation of parasitic capillaries under laboratory conditions into account},
	doi = {10.1109/IGARSS.2001.976695},
	author = {J. Klinke and Kudryavtsev, V. N. and Makin, V. K. and Bernd J{\"a}hne}
}
@article {marxen2000,
	title = {Comparison of Gaussian particle center estimators and the achievable measurement density for particle tracking velocimetry},
	journal = {Exp. Fluids},
	volume = {29},
	year = {2000},
	pages = {145-153},
	abstract = {A series of numerical simulations were conducted to investigate the performance of two particle center estimation algorithms for Particle Tracking Velocimetry: a simple three-point Gaussian estimator and a least-square Gaussian. The smallest position error for images with reasonable noise levels was found to be approximately 0.03 pixels for both estimators using particles with diameters of 4 pixels. As both estimators performed equally well, use of the simple three-point Gaussian algorithm is recommended because it executes 100 times faster than the least-square algorithm. The maximum achievable measurement density and accuracy for the three-point Gaussian estimator were determined with a numerical simulation of an Oseen vortex. Uncertainty measures have been introduced to filter out unreliable displacement measurements. It was found that 4 to 5 velocity vectors could be obtained within a 32{\texttimes}32 pixel area with an average displacement error of 0.1 pixels. This doubles the spatial resolution of conventional cross-correlation based Particle Image Velocimetry at comparable accuracy.},
	doi = {10.1007/s003489900085},
	author = {Michael Marxen and Sullivan, P. E. and Loewen, M. R. and Bernd J{\"a}hne}
}
@incollection {eichkorn2000,
	title = {Fluorescence imaging of air-water gas exchange},
	year = {2000},
	pages = {644-645},
	publisher = {Academic Press},
	chapter = {A18},
	author = {Sven Eichkorn and Thomas M{\"u}nsterer and Ulrike Lode and Bernd J{\"a}hne and Bernd J{\"a}hne and Horst Hau{\ss}ecker}
}
@article {uttenweiler2000,
	title = {Motion determination in actin filament fluorescence images with a spatio-temporal orientation analysis method.},
	journal = {Biophys J},
	volume = {78},
	number = {5},
	year = {2000},
	pages = {2709--2715},
	publisher = {Institut f{\"u}r Physiologie und Pathophysiologie, Ruprecht-Karls-Universit{\"a}t Heidelberg, 69120 Heidelberg, Germany. uttenweiler@urz.uni-heidelberg.de},
	abstract = {We present a novel approach of automatically measuring motion in series of microscopic fluorescence images. As a differential method, the three-dimensional structure tensor technique is used to calculate the displacement vector field for every image of the sequence, from which the velocities are subsequently derived. We have used this method for the analysis of the movement of single actin filaments in the in vitro motility assay, where fluorescently labeled actin filaments move over a myosin decorated surface. With its fast implementation and subpixel accuracy, this approach is, in general, very valuable for analyzing dynamic processes by image sequence analysis.},
	doi = {10.1016/S0006-3495(00)76815-9},
	author = {D. Uttenweiler and C. Veigel and R. Steubing and Carlo G{\"o}tz and Sven Mann and Horst Hau{\ss}ecker and Bernd J{\"a}hne and Rainer H. A. Fink}
}
@incollection {geissler2000c,
	title = {Three-dimensional imaging algorithms},
	year = {2000},
	pages = {397--438},
	publisher = {Academic Press},
	chapter = {11},
	author = {Peter Gei{\ss}ler and Tobias Dierig and Hanspeter A. Mallot and Bernd J{\"a}hne},
	editor = {Hau\DFecker, H.}
}
@inbook {stoehr1999,
	title = {4D particle tracking velocimetry applied to gas-liquid reactors},
	booktitle = {In Scientific Computing in Chemical Engineering II - Simulation, Image Processing, Optimization and Control.},
	year = {1999},
	pages = {270--279},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	author = {M. St{\"o}hr and Christoph S. Garbe and D. Engelmann and Peter Gei{\ss}ler and Gomes, S. and Frank Hering and Bernd J{\"a}hne and F. Keil and W. Mackens and H. Vo{\ss} and Wagner, H.-G..},
	editor = {J. Werther}
}
@incollection {noffz1999,
	title = {Field Programmable Gate Array image processing},
	volume = {3: Systems and Applications},
	year = {1999},
	publisher = {Academic Press},
	chapter = {2},
	author = {Noffz, K.-H. and Lay, R. and M{\"a}nner, R. and Bernd J{\"a}hne and Bernd J{\"a}hne and Peter Gei{\ss}ler and Horst Hau{\ss}ecker}
}
@incollection {eichkorn1999,
	title = {Fluorescenceimaging of air-water gas exchange},
	volume = {3: Systems and Applications},
	year = {1999},
	pages = {647-662},
	publisher = {Academic Press},
	chapter = {30},
	author = {Sven Eichkorn and Thomas M{\"u}nsterer and Ulrike Lode and Bernd J{\"a}hne and Bernd J{\"a}hne and Horst Hau{\ss}ecker and Peter Gei{\ss}ler}
}
@conference {blom1999,
	title = {Quality classification and process control of micro-spot laser welding},
	booktitle = {Proceedings of the Ninth International FAIM Conference - Flexible Automation and Intelligent Manufacturing, Tilburg},
	year = {1999},
	pages = {929--941},
	publisher = {Begell House},
	organization = {Begell House},
	author = {Blom, A. H. M and Brassel, Jan-Oliver and Brocke, Martin von and Mittler, M.},
	editor = {Ashayeri, Jalal and Sullivan, William and Ahmad, Munir}
}
@article {muensterer1998,
	title = {LIF measurements of concentration profiles in the aqueous mass boundary layer},
	journal = {Exp. Fluids},
	volume = {25},
	year = {1998},
	pages = {190--196},
	abstract = {A laser-induced fluorescence (LIF) technique is described to measure vertical concentration profiles of gases in the aqueous mass boundary layer at a free water surface. The technique uses an acid-base reaction of the fluorescence indicator fluorescein at the water surface to visualize the concentration profiles. The technique is capable of measuring two-dimensional vertical concentration profiles at a rate of 200 frames/s and a spatial resolution of 16 um. The mass boundary layer at a free surface is characterized by significant fluctuations. Direct surface renewal is observed. The mean profiles also support rather surface renewal models than turbulent diffusion models.},
	doi = {10.1007/s003480050223},
	author = {Thomas M{\"u}nsterer and Bernd J{\"a}hne}
}
@mastersthesis {mann1998,
	title = {Objektbasierte Bildfolgenanalyse zur Bewegungsbestimmung im in vitro Motility Assay unter Verwendung eines Strukturtensorverfahrens},
	year = {1998},
	school = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Sven Mann}
}
@mastersthesis {marxen1998,
	title = {Particle Image Velocimetry in Str{\"o}mungen mit starken Geschwindigkeitsgradienten},
	year = {1998},
	school = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Michael Marxen}
}
@article {kubinyi_98_threedimensional,
	title = {Threedimensional Quantitative Similarity-Activity Relationships (3DQSiAR) from SEAL Similarity Matrices},
	journal = {Journal of Medicinal Chemistry},
	volume = {41},
	year = {1998},
	pages = {2553-2564},
	author = {Kubinyi, H. and Fred A. Hamprecht and Mietzner, T.}
}
@incollection {liss1997,
	title = {Report Group 1 - Physical processes in the microlayer and the air-sea exchange of trace gases},
	year = {1997},
	pages = {1--33},
	publisher = {Cambridge University Press},
	doi = {10.1017/CBO9780511525025.002},
	author = {Peter S. Liss and A. J. Watson and E. J. Bock and Bernd J{\"a}hne and William E. Asher and Nelson M. Frew and L. Hasse and Gerald M. Korenowski and L. Merlivat and L. F. Phillips and P. Schl{\"u}ssel and D. K. Woolf},
	editor = {Peter S. Liss and Robert A. Duce}
}
@conference {hering1996b,
	title = {Combined wave and flow field visualization for investigation of short-wave/long-wave interaction},
	booktitle = {Proc.\ The Air-Sea Interface, Radio and Acoustic Sensing, Turbulence and Wave Dynamics, Marseille, 24--30. June 1993},
	year = {1996},
	pages = {133--138},
	publisher = {RSMAS, University of Miami},
	organization = {RSMAS, University of Miami},
	doi = {10.5281/zenodo.14002},
	author = {Frank Hering and Dietmar Wierzimok and W. K. Melville and Bernd J{\"a}hne},
	editor = {M. A. Donelan and W. H. Hui and W. J. Plant}
}
@conference {muensterer1996a,
	title = {A fluorescence technique to measure concentration profiles in the aqueous mass boundary layer},
	booktitle = {Proc.\ The Air-Sea Interface, Radio and Acoustic Sensing, Turbulence and Wave Dynamics, Marseille, 24--30. June 1993},
	year = {1996},
	pages = {517--522},
	publisher = {RSMAS, University of Miami},
	organization = {RSMAS, University of Miami},
	doi = {10.5281/zenodo.13995},
	author = {Thomas M{\"u}nsterer and Bernd J{\"a}hne},
	editor = {M. A. Donelan and W. H. Hui and W. J. Plant}
}
@phdthesis {muensterer1996,
	title = {LIF Investigation of the Mechanisms Controlling Air--Water Mass Transfer at a Free Interface},
	year = {1996},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	doi = {10.5281/zenodo.14542},
	author = {Thomas M{\"u}nsterer}
}
@conference {jaehne1996a,
	title = {The role of active vision in exploring growth, transport, and exchange processes},
	booktitle = {Aktives Sehen in technischen und biologischen Systemen, Workshop der GI-Fachgruppe 1.0.4. Bildverstehen Hamburg, 3--4. December 1996},
	volume = {4},
	year = {1996},
	pages = {194--202},
	publisher = {infix},
	organization = {infix},
	author = {Bernd J{\"a}hne and Horst Hau{\ss}ecker and Frank Hering and G{\"u}nther Balschbach and J. Klinke and Martin Lell and Dominik Schmund and Michael Schultz and Ulrich Schurr and Mark Stitt and Ulrich Platt},
	editor = {B Mertsching}
}
@proceedings {jaehne1995,
	title = {Air-Water Gas Transfer --- Selected Papers from the Third International Symposium on Air-Water Gas Transfer},
	year = {1995},
	publisher = {AEON},
	doi = {10.5281/zenodo.10571},
	url = {http://www.ub.uni-heidelberg.de/archiv/17063},
	author = {Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@mastersthesis {maier1995,
	title = {Aufbau einer aktiven Bildnachf{\"u}hrung zur Visualisierung von Wasserstr{\"o}mungen mit Tracerteilchen},
	year = {1995},
	note = {IUP D-447},
	school = {University of Heidelberg},
	author = {Maier, Christoph}
}
@conference {schmundt1995a,
	title = {The circular wind wave facilities at the University of Heidelberg},
	booktitle = {Air-Water Gas Transfer - Selected papers from the Third International Symposium on Air-Water Gas Transfer},
	year = {1995},
	pages = {505--516},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10404},
	author = {Dominik Schmund and Thomas M{\"u}nsterer and Lauer, H. and Bernd J{\"a}hne and Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@conference {vliet1995,
	title = {Delft Hydraulics Large Wind-Wave Flume},
	booktitle = {Air-Water Gas Transfer---Selected Papers from the Third International Symposium of Air--Water Gas Transfer in Heidelberg},
	year = {1995},
	pages = {499--502},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10402},
	author = {van Vliet, P. and Frank Hering and Bernd J{\"a}hne and Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@conference {bock1995b,
	title = {Description of the science plan for the April 1995 CoOP experiment, {\textquoteleft}gas transfer in coastal waters{\textquoteright}, performed from the research vessel New Horizon},
	booktitle = {Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer},
	year = {1995},
	pages = {801--810},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10408},
	author = {E. J. Bock and J. B. Edson and Nelson M. Frew and A. Karachintsev and W. R. McGilles and R. K. Nelson and K. Hansen and Bernd J{\"a}hne and T. Hara and B. M. Uz and Bernd J{\"a}hne and Jochen Dieter and J. Klinke and Horst Hau{\ss}ecker},
	editor = {E. C. Monahan}
}
@conference {muensterer1995,
	title = {Dual-tracer measurements of concentration profiles in the aqueous mass boundary layer},
	booktitle = {Air-water Gas Transfer, Selected Papers from the Third International Symposium on Air--Water Gas Transfer},
	year = {1995},
	pages = {637--648},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10406},
	author = {Thomas M{\"u}nsterer and Hans J{\"u}rgen Mayer and Bernd J{\"a}hne and Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@mastersthesis {mayer1995,
	title = {Entwicklung einer laserinduzierten Fluoreszenz-Technik zum Messen von Konzentrationsprofilen und Diffusionskonstanten},
	year = {1995},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ. Heidelberg},
	author = {Hans J{\"u}rgen Mayer}
}
@conference {hering1995c,
	title = {Flow- and wave-fields associated with micro-scale wave breaking as measured simultaneously by particle tracking and wave slope visualization},
	booktitle = {IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves},
	number = {19},
	year = {1995},
	pages = {385},
	abstract = {Longuet-Higgins predicted the development of a capillary roller at the crest of a short gravity wave, being the consequence of the existence of parasitic capillaries, (Capillary rollers and bores, J. Fluid Mech, Jan. 1992). According to the theory any free surface, at which the tangential stress vanishes, a surface vorticity is produced. The mean vorticity at the edge of the Stokes layer is of the order of 2(ak) f, ak being the slope and f the radian frequency of the wave. Hence capillary waves, with a high value of a are accompanied by particular large vorticity feeding the vortex roler at the crest of a short gravity wave. This phenomenon has been investigated at the circular facility of the Institute for Environmental Physics. At the same location, simultaneous time series of wave slope images and the Lagrangian flow field were obtained using an integrated optical set-up, including particle-tracking and wave-slope instruments. The particle tracking measurement yields drift- and bulk-velocities, Reynold stresses and the vorticity distribution beneath the wave, while the imaging wave slope gauges gives the 2D-slope and the height of the waves. First results from the measurement campaign of early 1995 will be presented.},
	author = {Frank Hering and G{\"u}nther Balschbach and M. Menzel and Dietmar Wierzimok and Bernd J{\"a}hne}
}
@conference {haussecker1995a,
	title = {Heat as a proxy tracer for gas exchange measurements in the field: principles and technical realization},
	booktitle = {Air--Water Gas Transfer: Selected Papers from the Third International Symposium on Air--Water Gas Transfer},
	year = {1995},
	pages = {405--413},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10401},
	author = {Horst Hau{\ss}ecker and Reinelt, S. and Bernd J{\"a}hne and Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@conference {haussecker1995c,
	title = {Horizontal and vertical spatial structures of turbulence beneath short wind waves},
	booktitle = {IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves},
	number = {19},
	year = {1995},
	pages = {384},
	abstract = {A poorly understood aspect of surface-wave physics is wave dissipation. Wave breaking may lead to enhanced turbulence levels and consequently enhanced viscous dissipation at the smallest scales. Recent laboratory, field and modelling studies have provided preliminary evidence that wave breaking may lead to dissipation levels one to two orders of magnitude greater than those in the classical logarithmic layer. However, the experimental techniques to study these phenomena are not well developed, and consequently the turbulence structure near the water surface is poorly understood. A method is described to simultaneously investigate the horizontal and vertical structure of the turbulent flow and wave fields at the water surface and in an intersecting two-dimensional vertical plane. The technique combines an active infrared technique (CFT) in the horizontal plane with digital particle imaging velocimetry (DPIV) in the vertical plane. The DPIV uses a CCD camera in an underwater housing to track submerged particles moving through a pulsed vertical laser light sheet aligned in the wind direction. The CFT uses infrared radiators to heat up the upper 20 micrometres of the water column and observes the resulting heat patterns with an infrared camera. Both instruments use digital image processing techniques to compute the flow variables and images of the turbulent structures. These initial experiments were conducted in the large wind-wave channel at Delft Hydraulics, The Netherlands, in September/October, 1994. The images show a dramatic transition from laminar to turbulent flow with the onset of significant wave activity coincident with vorticity transport from the surface to deeper layers.},
	author = {Horst Hau{\ss}ecker and R. Shear and W. K. Melville and Bernd J{\"a}hne}
}
@conference {jaehne1995b,
	title = {Impact of quantitative visualization and image processing on the study of small-scale air-sea interaction},
	booktitle = {Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer},
	year = {1995},
	pages = {3--12},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10326},
	author = {Bernd J{\"a}hne and Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@conference {haussecker1995,
	title = {In situ measurements of the air-sea gas transfer rate during the MBL/CoOP west coast experiment},
	booktitle = {Air-Water Gas Transfer - Selected Papers from the Third International Symposium on Air-Water Gas Transfer},
	year = {1995},
	pages = {775--784},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10407},
	author = {Horst Hau{\ss}ecker and Bernd J{\"a}hne and Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@conference {geissler1995,
	title = {Measurements of bubble size distributions with an optical technique based on depth from focus},
	booktitle = {Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer},
	year = {1995},
	pages = {351--362},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10400},
	author = {Peter Gei{\ss}ler and Bernd J{\"a}hne and Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@conference {hering1995b,
	title = {Measurements of enhanced turbulence in short wind-induced water waves},
	booktitle = {Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer},
	year = {1995},
	pages = {775--784},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10328},
	author = {Frank Hering and Dietmar Wierzimok and Bernd J{\"a}hne and Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@conference {klinke1995,
	title = {Measurements of short ocean waves during the MBL ARI West Coast Experiment},
	booktitle = {Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer},
	year = {1995},
	pages = {165--173},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10399},
	author = {J. Klinke and Bernd J{\"a}hne and Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@conference {dieter1995,
	title = {Measurements of velocity profiles in the aqueous boundary layer at a wind-driven water surface},
	booktitle = {Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer},
	year = {1995},
	pages = {145--152},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10393},
	author = {Jochen Dieter and Frank Hering and Roland Bremeyer and Bernd J{\"a}hne and Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@conference {balschbach1995,
	title = {A new instrument to measure steep wind-waves},
	booktitle = {IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves},
	number = {19},
	year = {1995},
	pages = {387},
	abstract = {Current instruments to measure the spatial structure of short waves are limited to quite low slopes. Capillary waves, however, may show slopes well above one (45 degrees inclination). A new instrument has been designed to measure the spatial structure of steep capillary waves. It was designed to specifically address a number of basic questions: Is the limiting form for Crapper capillary waves ever reached? Can a bubble be trapped by such a wave? Do solitary capillary/gravity waves - as recently postulated by Longuet-Higgins - exist in wind/wave fields? The instrument uses the basic design as used in previous instruments of our research group but includes two important enhancements. By the use of a telecentric illumination and camera lens, the influence of wave height on the slope measurement is eliminated. Colored light is used to encode both slope components simultaneously and to correct for uneven intensity distribution in the light source and intensity losses by reflection at the water surface. This contribution describes the setup of the instrument and its calibration in detail. First results from the circular wind/wave facility at Heidelberg University are reported in a separate paper.},
	author = {G{\"u}nther Balschbach and M. Menzel and Bernd J{\"a}hne}
}
@conference {hering1995a,
	title = {Particle tracking in space time sequences},
	booktitle = {Computer Analysis of Images and Patterns},
	volume = {970},
	year = {1995},
	pages = {294--301},
	publisher = {Springer},
	organization = {Springer},
	abstract = {A particle tracking technique at high particle concentration for the evaluation of flow fields beneath water waves is described. A 1-4 cm thick light sheet parallel to the main wave propagation direction was used to illuminate small polystyrol seeding particles. The depth of the light sheet was chosen such that the particles stay long enough in the illuminated area to enable tracking. An area of up to 14.0x10.0 cm2 is observed by a CCD camera. An automatic tracking algorithm is described to 800 particles/image, yielding both Lagrangian and Eulerian vector field. Test measurements show that the standard deviation for the velocity estimation is below 3\%.},
	doi = {10.1007/3-540-60268-2_309},
	author = {Frank Hering and Merle, M. and Dietmar Wierzimok and Bernd J{\"a}hne}
}
@conference {hering1995,
	title = {A robust technique for tracking particles over long image sequences},
	booktitle = {Proc. ISPRS Intercommission Workshop {\textquoteleft}From Pixels to Sequences{\textquoteright}, Zurich, March 22 - 24, 1995, In Int{\textquoteright}l Arch. of Photog. and Rem. Sens.},
	volume = {XXX-5W1},
	year = {1995},
	pages = {74--79},
	publisher = {RISC Books},
	organization = {RISC Books},
	doi = {10.5281/zenodo.14890},
	author = {Frank Hering and Merle, M. and Dietmar Wierzimok and Bernd J{\"a}hne},
	editor = {Baltsavias, E. P.}
}
@book {jaehne1996,
	title = {Technische Bildverarbeitung - Maschinelles Sehen},
	year = {1995},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-61403-3},
	url = {http://d-nb.info/94569895X},
	author = {Bernd J{\"a}hne and R. Massen and B. Nickolay and H. Scharfenberg}
}
@conference {frew1995a,
	title = {Variation of air--water gas transfer with wind stress and surface viscoelasticity},
	booktitle = {Air-water Gas Transfer, Selected Papers from the Third International Symposium on Air-Water Gas Transfer},
	year = {1995},
	pages = {529--541},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10405},
	author = {Nelson M. Frew and E. J. Bock and W. R. McGilles and A. Karachintsev and T. Hara and Thomas M{\"u}nsterer and Bernd J{\"a}hne and Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@conference {beyer1994,
	title = {Laboratory studies of long-wave/short-wave interaction using wavelet analysis of space-time images},
	booktitle = {Proc. 2nd Inter. Conf. on Air-Sea Interaction and on Meteorology and Oceanography of the Coastal Zone, Lisbon, 22.--27. September 1994},
	year = {1994},
	author = {Markus Beyer and Bernd J{\"a}hne and W. K. Melville}
}
@conference {muensterer1994,
	title = {A LIF technique for the measurement of concentration profiles in the aqueous mass boundary layer},
	booktitle = {Proc.\ 7th Intern.\ Symp.\ on Appl.\ of Laser Techn.\ to Fluid Mechanics, Lisbon, Portugal, July 11.--14. 1994},
	volume = {II},
	year = {1994},
	pages = {29.4.1--5},
	author = {Thomas M{\"u}nsterer and Bernd J{\"a}hne}
}
@mastersthesis {muensterer1993,
	title = {Messung von Konzentrationsprofilen gel{\"o}ster Gase in der wasserseitigen Grenzschicht},
	year = {1993},
	note = {IUP D-334},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	doi = {10.5281/zenodo.14538},
	author = {Thomas M{\"u}nsterer}
}
@article {jaehne1987b,
	title = {On the parameters influencing air-water gas exchange},
	journal = {J. Geophys. Res.},
	volume = {92},
	year = {1987},
	pages = {1937--1950},
	abstract = {Detailed gas exchange measurements from two circular and one linear wind/wave tunnels are presented. Heat, He, CH4, CO2, Kr, and Xe have been used as tracers. The experiments show the central importance of waves for the water-side transfer process. With the onset of waves the Schmidt number dependence of the transfer velocity k changes from k proportional to Sc^-2/3 to k proportional to Sc^-1/2 indicating a change in the boundary conditions at the surface. Moreover, energy put into the wave field by wind is transferred to near-surface turbulence enhancing gas transfer. The data show that the mean square slope of the waves is the best parameter to characterize the free wavy surface with respect to water-side transfer processes.},
	doi = {10.1029/JC092iC02p01937},
	author = {Bernd J{\"a}hne and K. O. M{\"u}nnich and R. B{\"o}singer and A. Dutzi and Werner A. Huber and P. Libner}
}
@article {broecker1986,
	title = {Isotopic versus micrometeorologic ocean CO$_2$ fluxes: A serious conflict},
	journal = {J. Geophys. Res.},
	volume = {91},
	number = {C9},
	year = {1986},
	pages = {10517--10528},
	abstract = {Eddy correlation measurements over the ocean give CO2 fluxes an order of magnitude or more larger than expected from mass balance measurements using radiocarbon and radon 222. In particular, Smith and Jones (1985) reported large upward and downward fluxes in a surf zone at supersaturations of 15\% and attributed them to the equilibration of bubbles at elevated pressures. They argue that even on the open ocean such bubble injection may create steady state CO2 supersaturations and that inferences of fluxes based on air-sea pCO2 differences and radon exchange velocities must be made with caution. We defend the global average CO2 exchange rate determined by three independent radioisotopic means: prebomb radiocarbon inventories; global surveys of mixed layer radon deficits; and oceanic uptake of bomb-produced radiocarbon. We argue that laboratory and lake data do not lead one to expect fluxes as large as reported from the eddy correlation technique; that the radon method of determining exchange velocities is indeed useful for estimating CO2 fluxes; that supersaturations of CO2 due to bubble injection on the open ocean are negligible; that the hypothesis that Smith and Jones advance cannot account for the fluxes that they report; and that the pC02 values reported by Smith and Jones are likely to be systematically much too high. The CO2 fluxes for the ocean measured to date by the micrometeorological method can be reconciled with neither the observed concentrations of radioisotopes of radon and carbon in the oceans nor the tracer experiments carried out in lakes and in wind/wave tunnels.},
	doi = {10.1029/JC091iC09p10517},
	author = {Broecker, W. S. and Ledwell, J. R. and Takahashi, T. and R. Weiss and L. Merlivat and L. Memery and Bernd J{\"a}hne and K. O. M{\"u}nnich}
}
@mastersthesis {maiss1986,
	title = {Modelluntersuchung zum Einfluss von Blasen auf den Gasaustausch zwischen Atmosph{\"a}re und Meer},
	year = {1986},
	note = {IUP D-215},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	doi = {10.5281/zenodo.15415},
	author = {Manfred Mai{\ss}}
}
@article {jaehne1985,
	title = {He and Rn gas exchange experiments in the large wind-wave facility of IMST},
	journal = {J. Geophys. Res.},
	volume = {90},
	year = {1985},
	pages = {11,989--11,998},
	abstract = {In a collaboration between the Laboratoire de G{\'e}ochimie Isotopique (Centre d{\textquoteright}Etudes Nucl{\'e}aires, Saclay), the Institut de M{\'e}canique Statistique de la Turbulence (IMST, Marseille), and the Institut f{\"u}r Umweltphysik (Heidelberg), for the first time gas exchange experiments have been carried out in the large IMST wind-wave facility. The experiments included simultaneous measurements of Rn and He gas exchange rates, wave slope measurements at four fetches, and bubble measurements. Compared with transfer velocities measured previously in smaller tunnels, our results are considerably lower. This effect can be explained qualitatively by differences in the wave field, which must be taken into account as an important parameter for gas exchange. Wave breaking, starting at 12 m/s wind, was not intense. Consequently, only low bubble densities are obtained, not significantly enhancing gas exchange.},
	doi = {10.1029/JC090iC06p11989},
	author = {Bernd J{\"a}hne and Thomas Wais and L. Memery and G. Caulliez and L. Merlivat and K. O. M{\"u}nnich and M. Coantic}
}
@incollection {jaehne1980,
	title = {Momentum induced gas exchange through a smooth water surface, models and experimental results from linear and circular wind-water tunnels},
	number = {17},
	year = {1980},
	pages = {55--62},
	publisher = {Univ. Hamburg},
	doi = {10.5281/zenodo.10257},
	author = {Bernd J{\"a}hne and K. O. M{\"u}nnich},
	editor = {H. C. Broecker and L. Hasse}
}
@article {jaehne1979,
	title = {Measurements of gas exchange and momentum transfer in a circular wind-water tunnel},
	journal = {Tellus},
	volume = {31},
	year = {1979},
	pages = {321--329},
	doi = {10.1111/j.2153-3490.1979.tb00911.x},
	author = {Bernd J{\"a}hne and K. O. M{\"u}nnich and U. Siegenthaler}
}
