@article {7049,
	title = {Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder},
	journal = {Bioinformatics},
	volume = {38 (Suppl 1)},
	number = { arXiv:2102.05892},
	year = {2022},
	pages = {i316-i324},
	publisher = {arXiv preprint},
	doi = {10.1093/bioinformatics/btac249 },
	author = {Garrido, Q and Damrich, S and J{\"a}ger, A and Cerletti, D and Claassen, M and Najman, L and Hamprecht, F A}
}
@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}
}
@article {nagel2019,
	title = {Measurement of air-sea gas transfer velocities in the Baltic Sea},
	journal = {Ocean Science},
	volume = {15},
	year = {2019},
	pages = {235{\textendash}247},
	doi = {10.5194/os-15-235-2019},
	author = {Leila Nagel and Kerstin E. Krall and Bernd J{\"a}hne}
}
@article {6337,
	title = {Total variation regularization of pose signals with an application to 3D freehand ultrasound},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {38(10)},
	year = {2019},
	pages = {2245-2258},
	doi = { 10.1109/TMI.2019.2898480},
	author = {Esposito, M and Hennersperger, C and G{\"o}bl, R and Demaret, L and Martin Storath and Navab, N and Baust, M and Weinmann, A}
}
@proceedings {6204,
	title = {Cost-efficient Gradient Boosting},
	year = {2017},
	author = {Peter, S. and Ferran Diego and Fred A. Hamprecht and Nadler, B}
}
@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 In recent years, traditionally hand-crafted pipelines have been replaced by deep learning pipelines, which can be trained in an end-to-end fashion.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 {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}
}
@article {Berger2017,
	title = {{Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations},
	journal = {J. Math. Imag. Vision},
	volume = {58},
	number = {1},
	year = {2017},
	pages = {102{\textendash}129},
	author = {Johannes Berger and Lenzen, F. and Florian Becker and Neufeld, A. and Schn{\"o}rr, C.}
}
@mastersthesis {6219,
	title = {Self-Similarity Based Detection of Temporal Motifs in Multivariate Signals},
	year = {2017},
	school = {Heidelberg University},
	author = {Neigel, P}
}
@article {baust2016combined,
	title = {Combined Tensor Fitting and TV Regularization in Diffusion Tensor Imaging based on a Riemannian Manifold Approach},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {35},
	year = {2016},
	pages = {1972{\textendash}1989},
	doi = {10.1109/TMI.2016.2528820},
	author = {Maximilian Baust and Andreas Weinmann and Matthias Wieczorek and Tobias Lasser and Martin Storath and Nassir Navab}
}
@conference {Kondermann_2016_CVPR_Workshops,
	title = {The HCI Benchmark Suite: Stereo and Flow Ground Truth With Uncertainties for Urban Autonomous Driving},
	booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
	year = {2016},
	month = {June},
	abstract = { Recent advances in autonomous driving require more and more highly realistic reference data, even for difficult situations such as low light and bad weather. We present a new stereo and optical flow dataset to complement existing benchmarks. It was specifically designed to be representative for urban autonomous driving, including realistic, systematically varied radiometric and geometric challenges which were previously unavailable. The accuracy of the ground truth is evaluated based on Monte Carlo simulations yielding full, per-pixel distributions. Interquartile ranges are used as uncertainty measure to create binary masks for arbitrary accuracy thresholds and show that we achieved uncertainties better than those reported for comparable outdoor benchmarks. Binary masks for all dynamically moving regions are supplied with estimated stereo and flow values. An initial public benchmark dataset of 55 manually selected sequences between 19 and 100 frames long are made available in a dedicated website featuring interactive tools for database search, visualization, comparison and benchmarking.
},
	author = {Daniel Kondermann and Nair, Rahul and Katrin Honauer and Karsten Krispin and Jonas Andrulis and Alexander Brock and G{\"u}ssefeld, Burkhard and Mohsen Rahimimoghaddam and Sabine Hofmann and Brenner, Claus and Bernd J{\"a}hne}
}
@article {biller_16_improved,
	title = {Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression},
	journal = {American Journal of Neuroradiology},
	volume = {37 },
	year = {2016},
	pages = {66-73},
	doi = {10.3174/ajnr.A4493},
	author = {A. Biller and Badde, S and Nagel, A and Neumann, JO and Wick, W and Hertenstein, A and M. Bendszus and Sahm, F and Benkhedah, N and Kleesiek, J}
}
@article {7468481,
	title = {Joint Segmentation and Shape Regularization with a Generalized Forward Backward Algorithm},
	journal = {IEEE Transactions on Image Processing},
	volume = {25},
	year = {2016},
	pages = {3384 - 3394},
	keywords = {Active contours, Computational modeling, Image segmentation, Manifolds, Shape, Three-dimensional displays, TV},
	issn = {1057-7149},
	doi = {10.1109/TIP.2016.2567068},
	author = {Anca Stefanoiu and Andreas Weinmann and Martin Storath and Nassir Navab and Maximilian Baust}
}
@phdthesis {nair2015,
	title = {Analysis and Modeling of Passive Stereo and Time-of-Flight Imaging},
	volume = {Dissertation},
	year = {2015},
	publisher = {IWR, Univ. Heidelberg},
	type = {phdDissertation},
	doi = {10.11588/heidok.00018174},
	author = {Rahul Nair}
}
@article {krall2015,
	title = {Comparative heat and gas exchange measurements in the Heidelberg Aeolotron, a large annular wind-wave tank},
	journal = {Ocean Sci.},
	volume = {11},
	year = {2015},
	pages = {111--120},
	doi = {10.5194/os-11-111-2015},
	author = {Leila Nagel and Kerstin Ellen Krall and Bernd J{\"a}hne}
}
@article {kappes_15_comparative,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	year = {2015},
	note = {1},
	pages = {1-30},
	doi = {10.1007/s11263-015-0809-x},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, S. and Dhruv Batra and Kim, S. and Bernhard X. Kausler and Thorben Kr{\"o}ger and Lellmann, J. and Komodakis, N. and Savchynskyy, B. and Carsten Rother}
}
@article {Kappes2015,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {Int.~J.~Comp.~Vision},
	year = {2015},
	note = {in press (preprint: arXiv:1404.0533)},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, S. and Dhruv Batra and Kim, S. and Bernhard X. Kausler and Thorben Kr{\"o}ger and Lellmann, J. and Komodakis, N. and Savchynskyy, B. and Carsten Rother}
}
@article {Kappes2015,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	volume = {115},
	number = {2},
	year = {2015},
	pages = {155{\textendash}184},
	abstract = {Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.},
	keywords = {Benchmark, Combinatorial optimization, Discrete graphical models},
	issn = {15731405},
	doi = {10.1007/s11263-015-0809-x},
	url = {http://hci.iwr.uni-heidelberg.de/opengm2/},
	author = {Kappes, J{\"o}rg H and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Kausler, Bernhard X and Kr{\"o}ger, Thorben and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@article {Kappes2015a,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	volume = {115},
	number = {2},
	year = {2015},
	pages = {155{\textendash}184},
	abstract = {Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.},
	keywords = {Benchmark, Combinatorial optimization, Discrete graphical models},
	isbn = {25164671.25},
	issn = {15731405},
	doi = {10.1007/s11263-015-0809-x},
	author = {Kappes, J{\"o}rg H and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Kausler, Bernhard X and Kr{\"o}ger, Thorben and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@article {Kappes2015b,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	volume = {115},
	number = {2},
	year = {2015},
	pages = {155{\textendash}184},
	abstract = {Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.},
	keywords = {Benchmark, Combinatorial optimization, Discrete graphical models},
	issn = {15731405},
	doi = {10.1007/s11263-015-0809-x},
	author = {Kappes, J{\"o}rg H and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Kausler, Bernhard X and Kr{\"o}ger, Thorben and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@conference {Neufeld-et-al-2015a,
	title = {Estimating Vehicle Ego-Motion and Piecewise Planar Scene Structure from Optical Flow in a Continuous Framework},
	booktitle = {37th German Conference on Pattern Recognition},
	year = {2015},
	note = {in press},
	author = {Neufeld, Andreas and Johannes Berger and Florian Becker and Lenzen, Frank and Christoph Schn{\"o}rr}
}
@conference {Schelten2015,
	title = {Interleaved regression tree field cascades for blind image deconvolution},
	booktitle = {Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015},
	year = {2015},
	pages = {494{\textendash}501},
	abstract = {Image blur from camera shake is a common cause for poor image quality in digital photography, prompting a significant recent interest in image deblurring. The vast majority of work on blind deblurring splits the problem into two subsequent steps: First, the blur process (i.e., blur kernel) is estimated, then the image is restored given the estimated kernel using a non-blind deblurring algorithm. Recent work in non-blind deblurring has shown that discriminative approaches can have clear image quality and runtime benefits over typical generative formulations. In this paper, we propose a cascade for blind deblurring that alternates between kernel estimation and discriminative deblurring using regression tree fields (RTFs). We further contribute a new dataset of realistic image blur kernels from human camera shake, which we use to train the discriminative component. Extensive qualitative and quantitative experiments show a clear gain in image quality by interleaving kernel estimation and discriminative deblurring in an iterative cascade.},
	isbn = {9781479966820},
	doi = {10.1109/WACV.2015.72},
	author = {Schelten, Kevin and Nowozin, Sebastian and Jancsary, Jeremy and Carsten Rother and Roth, Stefan}
}
@conference {Nair2015,
	title = {Reflection modeling for passive stereo},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2015 Inter},
	year = {2015},
	pages = {2291{\textendash}2299},
	abstract = {Stereo reconstruction in presence of reality faces many challenges that still need to be addressed. This paper considers reflections, which introduce incorrect matches due to the observation violating the diffuse-world assumption underlying the majority of stereo techniques. Unlike most existing work, which employ regularization or robust data terms to suppress such errors, we derive two least squares models from first principles that generalize diffuse world stereo and explicitly take reflections into account. These models are parametrized by depth, orientation and material properties, resulting in a total of up to 5 parameters per pixel that have to be estimated. Additionally large non-local interactions between viewed and reflected surface have to be taken into account. These two properties make inference of the model appear prohibitive, but we present evidence that inference is actually possible using a variant of patch match stereo.},
	isbn = {9781467383912},
	issn = {15505499},
	doi = {10.1109/ICCV.2015.264},
	author = {Nair, Rahul and Fitzgibbon, Andrew and Kondermann, Daniel and Carsten Rother}
}
@conference {Berger-et-al-2015a,
	title = {Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations},
	booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM 2015)},
	year = {2015},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {Accurate camera motion estimation is a fundamental build- ing block for many Computer Vision algorithms. For improved robustness, temporal consistency of translational and rotational camera velocity is often assumed by propagating motion information forward using stochastic filters. Classical stochastic filters, however, use linear approximations for the non-linear observer model and for the non-linear structure of the underlying Lie Group SE(3) and have to approximate the unknown posteriori distribution. In this paper we employ a non-linear measurement model for the camera motion estimation problem that incorporates multiple observation equations. We solve the underlying filtering problem using a novel Minimum Energy Filter on SE(3) and give explicit expressions for the optimal state variables. Experiments on the challenging KITTI benchmark show that, although a simple motion model is only employed, our approach improves rotational velocity esti- mation and otherwise is on par with the state-of-the-art.},
	doi = {10.1007/978-3-319-18461-6_32},
	url = {http://dx.doi.org/10.1007/978-3-319-18461-6_32},
	author = {Johannes Berger and Andreas Neufeld and Florian Becker and Frank Lenzen and Christoph Schn{\"o}rr}
}
@conference {Berger-et-al-2015a,
	title = {Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations},
	booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM 2015)},
	year = {2015},
	author = {Johannes Berger and Neufeld, Andreas and Florian Becker and Lenzen, Frank and Christoph Schn{\"o}rr}
}
@booklet {Berger-et-al-2015b,
	title = {Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations},
	year = {2015},
	abstract = {Camera motion estimation from observed scene features is an important task in image processing to increase the accuracy of many methods, e.g. optical flow and structure-from-motion. Due to the curved geometry of the state space SE(3) and the non-linear relation to the observed optical flow, many recent filtering approaches use a first-order approximation and assume a Gaussian a posteriori distribution or restrict the state to Euclidean geometry. The physical model is usually also limited to uniform motions. 
We propose a second-order minimum energy filter with a generalized kinematic model that copes with the full geometry of SE(3) as well as with the nonlinear dependencies between the state space and observations. The derived filter enables reconstructing motions correctly for synthetic and real scenes, e.g. from the KITTI benchmark. Our experiments confirm that the derived minimum energy filter with higher-order state differential equation copes with higher-order kinematics and is also able to minimize model noise. We also show that the proposed filter is superior to state-of-the-art extended Kalman filters on Lie groups in the case of linear observations and that our method reaches the accuracy of modern visual odometry methods.},
	keywords = {Constant Acceleration Model, Lie Group, Minimum Energy Filter, Optimal Control, Recursive Filtering, Visual Odometry},
	url = {http://arxiv.org/abs/1507.06810},
	author = {Johannes Berger and Frank Lenzen and Florian Becker and Andreas Neufeld and Christoph Schn{\"o}rr}
}
@booklet {Berger-et-al-2015b,
	title = {Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations},
	year = {2015},
	note = {ArXiv, preprint},
	url = {http://arxiv.org/abs/1507.06810},
	author = {Johannes Berger and Lenzen, Frank and Florian Becker and Neufeld, Andreas and Christoph Schn{\"o}rr}
}
@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}
}
@phdthesis {nagel2014,
	title = {Active Thermography to Investigate Small-Scale Air-Water Transport Processes in the Laboratory and the Field},
	volume = {Dissertation},
	year = {2014},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Chemie und Geowissenschaften, Univ.\ Heidelberg},
	url = {http://www.ub.uni-heidelberg.de/archiv/16831},
	author = {Leila Nagel}
}
@article {nagel2014a,
	title = {Comparative heat and gas exchange measurements in the Heidelberg Aeolotron, a large annular wind-wave tank},
	journal = {Ocean Sci. Discuss.},
	volume = {11},
	year = {2014},
	pages = {1691--1718},
	doi = {10.5194/osd-11-1691-2014},
	author = {Leila Nagel and Kerstin Ellen Krall and Bernd J{\"a}hne}
}
@article {kappes_14_comparative,
	title = {A Comparative Study of Modern Inference Techniques for Structured
Discrete Energy Minimization Problems},
	journal = {CoRR},
	year = {2014},
	note = {1},
	url = {http://arxiv.org/abs/1404.0533},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, S. and Dhruv Batra and Kim, S. and Bernhard X. Kausler and Thorben Kr{\"o}ger and Lellmann, J. and Komodakis, N. and Savchynskyy, B. and Carsten Rother}
}
@article {kappes-1014-bench-arxiv,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {CoRR},
	volume = {abs/1404.0533},
	year = {2014},
	url = {http://hci.iwr.uni-heidelberg.de/opengm2/},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Bernhard X. Kausler and Thorben Kr{\"o}ger and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@conference {esparza2014b,
	title = {Extrinsic calibration of a fisheye multi-camera setup using overlapping fields of view},
	booktitle = {Intelligent Vehicles Symposium Proceedings},
	year = {2014},
	pages = {1276--1281},
	doi = {10.1109/IVS.2014.6856403},
	author = {Knorr, M. and Esparza, J. and Niehsen, W. and Stiller, C.}
}
@conference {kraeuter2014a,
	title = {High-resolution 2-D fluorescence imaging of gas transfer at a free water surface},
	booktitle = {Ocean Science Meeting, 23--28. 02. 2014, Honolulu Hawaii},
	year = {2014},
	abstract = {A novel 2-D fluorescence imaging technique has been developed to visualize gas exchange between air and water using ammonia as a tracer. Fluorescence is stimulated by high-power LEDs and is observed from above with a low-noise, high-resolution and high-speed camera. The invasion of ammonia into water leads to an increase in pH (from a starting value of 4), which is visualized with a fluorescent dye. The flux of ammonia can be controlled by controlling its air-side concentration. A higher flux leads to an increase of the thickness of the layer, from which fluorescent light is emitted (pH > 7). In this way, a varying fraction of the thickness of the aqueous mass boundary layer is imaged. In addition to the fluorescence measurement, we conducted collocated and simultaneous thermography and wave imaging measurements. With this data set, it is possible to compare heat and gas transfer and to investigate the effect of waves on both transfer processes. First results will be presented.},
	doi = {10.5281/zenodo.12330},
	author = {Christine Kr{\"a}uter and Darya Trofimova and Leila Nagel and Bernd J{\"a}hne}
}
@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}
}
@incollection {lou_14_structured,
	title = {Structured Learning from Cheap Data},
	year = {2014},
	note = {1},
	publisher = {The MIT Press},
	author = {Lou, X. and Kloft, M. and R{\"a}tsch, G. and Fred A. Hamprecht},
	editor = {Nowozin, S. et al}
}
@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}
}
@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}
}
@conference {kappes_13_comparative,
	title = {A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems},
	booktitle = {CVPR 2013. Proceedings},
	year = {2013},
	note = {1},
	doi = {10.1109/CVPR.2013.175},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, S. and Dhruv Batra and Sungwoong, K. and Bernhard X. Kausler and Lellmann, J. and Komodakis, N. and Carsten Rother}
}
@conference {Kappes-2013-benchmark,
	title = {A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem},
	booktitle = {CVPR},
	year = {2013},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Bernhard X. Kausler and Lellmann, Jan and Komodakis, Nikos and Carsten Rother}
}
@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}
}
@techreport {Schmidt,
	title = {Discriminative Non-blind Deblurring},
	year = {2013},
	abstract = {Non-blind deblurring is an integral component of blind approaches for removing image blur due to camera shake. Even though learning-based deblurring methods exist, they have been limited to the generative case and are compu-tationally expensive. To this date, manually-defined models are thus most widely used, though limiting the attained restoration quality. We address this gap by proposing a dis-criminative approach for non-blind deblurring. One key challenge is that the blur kernel in use at test time is not known in advance. To address this, we analyze existing approaches that use half-quadratic regularization. From this analysis, we derive a discriminative model cascade for image deblurring. Our cascade model consists of a Gaus-sian CRF at each stage, based on the recently introduced regression tree fields. We train our model by loss minimization and use synthetically generated blur kernels to generate training data. Our experiments show that the proposed approach is efficient and yields state-of-the-art restoration quality on images corrupted with synthetic and real blur.},
	author = {Schmidt, Uwe and Carsten Rother and Nowozin, Sebastian and Jancsary, Jeremy and Roth, Stefan}
}
@conference {haeusler_13_ensemble,
	title = {Ensemble Learning for Confidence Measures in Stereo Vision},
	booktitle = {CVPR 2013, in press},
	year = {2013},
	note = {1},
	pages = {305-312},
	doi = {10.1109/CVPR.2013.46},
	author = {Haeusler, R. and Nair, R. and Daniel Kondermann}
}
@conference {Jancsary2013,
	title = {Learning convex QP relaxations for structured prediction},
	booktitle = {30th International Conference on Machine Learning, ICML 2013},
	number = {PART 3},
	year = {2013},
	pages = {1952{\textendash}1960},
	abstract = {We introduce a new large margin approach to discriminative training of intractable discrete graphical models. Our approach builds on a convex quadratic programming relaxation of the MAP inference problem. The model parameters are trained directly within this restricted class of energy functions so as to optimize the predictions on the training data. We address the issue of how to parameterize the resulting model and point out its relation to existing approaches. The primary motivation behind our use of the QP relaxation is its computational efficiency; yet, empirically, its predictive accuracy compares favorably to more expensive approaches. This makes it an appealing choice for many practical tasks. Copyright 2013 by the author(s).},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Carsten Rother}
}
@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}
}
@conference {lefloch_13_technical,
	title = {Technical Foundation and Calibration Methods for Time-of-Flight Cameras},
	booktitle = {Time-of-Flight Imaging: Algorithms, Sensors and Applications},
	volume = {8200},
	number = {3-24},
	year = {2013},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-44964-2_1},
	author = {Lefloch, D. and Nair, R. and Frank Lenzen and Sch{\"a}fer, H. and Streeter, L. and Michael J. Cree},
	editor = {Reinhard Koch and Grzegorzek, M. and Theobalt, C. and Andreas Kolb and Andreas Kolb and Reinhard Koch}
}
@incollection {Lefloch2013foundations,
	title = {Technical Foundation and Calibration Methods for Time-of-Flight Cameras},
	volume = {8200},
	year = {2013},
	pages = {3-24},
	publisher = {Springer},
	author = {Damien Lefloch and Rahul Nair and Frank Lenzen and Henrik Sch{\"a}fer and Lee Streeter and Michael J. Cree and Reinhard Koch and Andreas Kolb},
	editor = {Grzegorzek, Marcin and Theobalt, Christian and Koch, Reinhard and Kolb, Andreas}
}
@article {roeder_12_active,
	title = {Active Learning with Distributional Estimates},
	journal = {UAI 2012. Proceedings},
	year = {2012},
	note = {1},
	pages = {715-725},
	author = {R{\"o}der, J. and Kunzmann, K. and Nadler, B. and Fred A. Hamprecht}
}
@conference {nagel2012,
	title = {Active Thermography as a Link between Laboratory and Field Studies},
	booktitle = {SOLAS Open Science Conference, Washington State, USA},
	year = {2012},
	author = {Leila Nagel and Kerstin E. Richter and Bernd J{\"a}hne}
}
@conference {andres_12_globally,
	title = {Globally Optimal Closed-Surface Segmentation for Connectomics},
	booktitle = {ECCV 2012. Proceedings, Part 3},
	number = {7574},
	year = {2012},
	note = {1},
	pages = {778-791},
	doi = {10.1007/978-3-642-33712-3_56},
	author = {Bj{\"o}rn Andres and Thorben Kr{\"o}ger and K. L. Briggmann and Denk, W. and Norogod, N. and G. W. Knott and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@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 {jaehne2012g,
	title = {Investigation of small-scale air-sea interaction processes by active thermography},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS)},
	year = {2012},
	pages = {358--361},
	doi = {10.1109/IGARSS.2012.6351564},
	author = {Bernd J{\"a}hne and Leila Nagel}
}
@article {fiaschi_12_learning,
	title = {Learning to Count with Regression Forest and Structured Labels},
	journal = {ICPR 2012. Proceedings},
	year = {2012},
	note = {1},
	pages = {2685-2688},
	author = {Fiaschi, L. and Nair, R. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Jancsary2012,
	title = {Loss-specific training of non-parametric image restoration models: A new state of the art},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {7578 LNCS},
	number = {PART 7},
	year = {2012},
	pages = {112{\textendash}125},
	abstract = {After a decade of rapid progress in image denoising, recent methods seem to have reached a performance limit. Nonetheless, we find that state-of-the-art denoising methods are visually clearly distinguishable and possess complementary strengths and failure modes. Motivated by this observation, we introduce a powerful non-parametric image restoration framework based on Regression Tree Fields (RTF). Our restoration model is a densely-connected tractable conditional random field that leverages existing methods to produce an image-dependent, globally consistent prediction. We estimate the conditional structure and parameters of our model from training data so as to directly optimize for popular performance measures. In terms of peak signal-to-noise-ratio (PSNR), our model improves on the best published denoising method by at least 0.26dB across a range of noise levels. Our most practical variant still yields statistically significant improvements, yet is over 20x faster than the strongest competitor. Our approach is well-suited for many more image restoration and low-level vision problems, as evidenced by substantial gains in tasks such as removal of JPEG blocking artefacts. {\textcopyright} 2012 Springer-Verlag.},
	isbn = {9783642337857},
	issn = {03029743},
	doi = {10.1007/978-3-642-33786-4_9},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Carsten Rother}
}
@conference {Jancsary2012a,
	title = {Loss-specific training of non-parametric image restoration models: A new state of the art},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {7578 LNCS},
	number = {PART 7},
	year = {2012},
	pages = {112{\textendash}125},
	abstract = {After a decade of rapid progress in image denoising, recent methods seem to have reached a performance limit. Nonetheless, we find that state-of-the-art denoising methods are visually clearly distinguishable and possess complementary strengths and failure modes. Motivated by this observation, we introduce a powerful non-parametric image restoration framework based on Regression Tree Fields (RTF). Our restoration model is a densely-connected tractable conditional random field that leverages existing methods to produce an image-dependent, globally consistent prediction. We estimate the conditional structure and parameters of our model from training data so as to directly optimize for popular performance measures. In terms of peak signal-to-noise-ratio (PSNR), our model improves on the best published denoising method by at least 0.26dB across a range of noise levels. Our most practical variant still yields statistically significant improvements, yet is over 20x faster than the strongest competitor. Our approach is well-suited for many more image restoration and low-level vision problems, as evidenced by substantial gains in tasks such as removal of JPEG blocking artefacts. {\textcopyright} 2012 Springer-Verlag.},
	isbn = {9783642337857},
	issn = {03029743},
	doi = {10.1007/978-3-642-33786-4_9},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Carsten Rother}
}
@conference {Jancsary2012b,
	title = {Non-parametric crfs for image labeling},
	booktitle = {NIPS Workshop Modern Nonparametric Methods in Machine Learning},
	number = {2},
	year = {2012},
	pages = {1{\textendash}5},
	abstract = {We introduce a powerful non-parametric image labeling framework, Regression Tree Fields (RTFs), and discuss its application to image restoration. The conditional structure and the parameters of our model are estimated from training data so as to directly optimize for popular performance measures, resulting in excellent predictive performance at low computational cost.},
	url = {http://www.nowozin.net/sebastian/papers/jancsary2012nonparametriccrf.pdf},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Carsten Rother}
}
@conference {Jancsary2012c,
	title = {Regression Tree Fields An efficient, non-parametric approach to image labeling problems},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2012},
	pages = {2376{\textendash}2383},
	abstract = {We introduce Regression Tree Fields (RTFs), a fully conditional random field model for image labeling problems. RTFs gain their expressive power from the use of non-parametric regression trees that specify a tractable Gaussian random field, thereby ensuring globally consistent predictions. Our approach improves on the recently introduced decision tree field (DTF) model [14] in three key ways: (i) RTFs have tractable test-time inference, making efficient optimal predictions feasible and orders of magnitude faster than for DTFs, (ii) RTFs can be applied to both discrete and continuous vector-valued labeling tasks, and (Hi) the entire model, including the structure of the regression trees and energy function parameters, can be efficiently and jointly learned from training data. We demonstrate the expressive power and flexibility of the RTF model on a wide variety of tasks, including inpainting, colorization, denoising, and joint detection and registration. We achieve excellent predictive performance which is on par with, or even surpassing, DTFs on all tasks where a comparison is possible. {\textcopyright} 2012 IEEE.},
	isbn = {9781467312264},
	issn = {10636919},
	doi = {10.1109/CVPR.2012.6247950},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Sharp, Toby and Carsten Rother}
}
@conference {Jancsary2012d,
	title = {Regression Tree Fields An efficient, non-parametric approach to image labeling problems},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2012},
	pages = {2376{\textendash}2383},
	abstract = {We introduce Regression Tree Fields (RTFs), a fully conditional random field model for image labeling problems. RTFs gain their expressive power from the use of non-parametric regression trees that specify a tractable Gaussian random field, thereby ensuring globally consistent predictions. Our approach improves on the recently introduced decision tree field (DTF) model [14] in three key ways: (i) RTFs have tractable test-time inference, making efficient optimal predictions feasible and orders of magnitude faster than for DTFs, (ii) RTFs can be applied to both discrete and continuous vector-valued labeling tasks, and (Hi) the entire model, including the structure of the regression trees and energy function parameters, can be efficiently and jointly learned from training data. We demonstrate the expressive power and flexibility of the RTF model on a wide variety of tasks, including inpainting, colorization, denoising, and joint detection and registration. We achieve excellent predictive performance which is on par with, or even surpassing, DTFs on all tasks where a comparison is possible. {\textcopyright} 2012 IEEE.},
	isbn = {9781467312264},
	issn = {10636919},
	doi = {10.1109/CVPR.2012.6247950},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Sharp, Toby and Carsten Rother}
}
@article {Kohli2012,
	title = {User-centric learning and evaluation of interactive segmentation systems},
	journal = {International Journal of Computer Vision},
	volume = {100},
	number = {3},
	year = {2012},
	month = {dec},
	pages = {261{\textendash}274},
	abstract = {Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. In this paper, we study the problem of evaluating and learning interactive segmentation systems which are extensively used in the real world. The key questions in this context are how to measure (1) the effort associated with a user interaction, and (2) the quality of the segmentation result as perceived by the user. We conduct a user study to analyze user behavior and answer these questions. Using the insights obtained from these experiments, we propose a framework to evaluate and learn interactive segmentation systems which brings the user in the loop. The framework is based on the use of an active robot user-a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem. {\textcopyright} 2012 The Author(s).},
	keywords = {Image segmentation, Interactive systems, Learning},
	issn = {09205691},
	doi = {10.1007/s11263-012-0537-4},
	author = {Kohli, Pushmeet and Nickisch, Hannes and Carsten Rother and Rhemann, Christoph}
}
@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 {Nowozin2011a,
	title = {Decision tree fields},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	year = {2011},
	pages = {1668{\textendash}1675},
	abstract = {This paper introduces a new formulation for discrete image labeling tasks, the Decision Tree Field (DTF), that combines and generalizes random forests and conditional random fields (CRF) which have been widely used in computer vision. In a typical CRF model the unary potentials are derived from sophisticated random forest or boosting based classifiers, however, the pairwise potentials are assumed to (1) have a simple parametric form with a pre-specified and fixed dependence on the image data, and (2) to be defined on the basis of a small and fixed neighborhood. In contrast, in DTF, local interactions between multiple variables are determined by means of decision trees evaluated on the image data, allowing the interactions to be adapted to the image content. This results in powerful graphical models which are able to represent complex label structure. Our key technical contribution is to show that the DTF model can be trained efficiently and jointly using a convex approximate likelihood function, enabling us to learn over a million free model parameters. We show experimentally that for applications which have a rich and complex label structure, our model achieves excellent results. {\textcopyright} 2011 IEEE.},
	isbn = {9781457711015},
	doi = {10.1109/ICCV.2011.6126429},
	author = {Nowozin, Sebastian and Carsten Rother and Bagon, Shai and Sharp, Toby and Yao, Bangpeng and Kohli, Pushmeet}
}
@article {Nicola-et-al-2011,
	title = {A general extending and constraining procedure for linear iterative methods},
	journal = {Int.~J.~Comp.~Math.},
	year = {2011},
	note = {in press},
	url = {http://dx.doi.org/10.1080/00207160.2011.634002},
	author = {Nicola, A. and Stefania Petra and Popa, C. and Christoph Schn{\"o}rr}
}
@article {Nicola-et-al-2011,
	title = {A general extending and constraining procedure for linear iterative methods},
	journal = {Int. J. Comp. Math.},
	year = {2011},
	note = {in press},
	url = {http://dx.doi.org/10.1080/00207160.2011.634002},
	author = {Nicola, A. and Petra, S. and Popa, C. and Schn{\"o}rr, C.}
}
@article {nair_11_high,
	title = {High Precision TOF-guided Depth from Stereo for Room Scanning},
	journal = {CVMP, Proceedings.},
	year = {2011},
	note = {1},
	author = {Nair, R. and Daniel Kondermann}
}
@conference {schimpf2011,
	title = {Lock-in thermography at the ocean surface: a local and fast method to investigate heat and gas exchange between ocean and atmosphere},
	booktitle = {DPG Fr{\"u}hjahrstagung Dresden, Fachverband Umweltphysik},
	year = {2011},
	abstract = {Heat is used as a proxy tracer for gases to study the transport processes across the sea-surface interface to obtain a detailed insight into the diffusive and turbulent processes controlling the transport. A carbon dioxide laser forces a periodically varying heat flux density onto the water surface and the amplitude damping and phase shift of the sea surface temperature is measured from infrared image sequences. The transport process can be treated by linear system theory and the relation between the input signal (periodically varying surface flux density) and the output (surface temperature) is estimated. Within the framework of the SOPRAN initiative three field experiments in the Baltic Sea were conducted. The locally derived heat transfer rates are scaled to gas transfer rates, which are in good agreement with empirical gas transfer wind speed relationships for moderate winds speeds. At high wind speed, the transfer rates are lower, which is explained by the fact that heat transport is insensitive to bubble-mediated gas transfer, i.e. it measures only a part of the transfer process directly at the water surface. Together with eddy covariance measurements a significant improvement of the parameterization of heat and gas transfer velocities can be expected.},
	url = {http://www.dpg-verhandlungen.de/year/2011/conference/dresden/part/up/session/1/contribution/28},
	author = {Uwe Schimpf and Leila Nagel and Bernd J{\"a}hne}
}
@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 {Pletscher2011,
	title = {Putting MAP back on the map},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6835 LNCS},
	year = {2011},
	pages = {111{\textendash}121},
	abstract = {Conditional Random Fields (CRFs) are popular models in computer vision for solving labeling problems such as image denoising. This paper tackles the rarely addressed but important problem of learning the full form of the potential functions of pairwise CRFs. We examine two popular learning techniques, maximum likelihood estimation and maximum margin training. The main focus of the paper is on models such as pairwise CRFs, that are simplistic (misspecified) and do not fit the data well. We empirically demonstrate that for misspecified models maximum-margin training with MAP prediction is superior to maximum likelihood estimation with any other prediction method. Additionally we examine the common belief that MLE is better at producing predictions matching image statistics. {\textcopyright} 2011 Springer-Verlag.},
	isbn = {9783642231223},
	issn = {03029743},
	doi = {10.1007/978-3-642-23123-0_12},
	author = {Pletscher, Patrick and Nowozin, Sebastian and Kohli, Pushmeet and Carsten Rother}
}
@conference {Pletscher2011a,
	title = {Putting MAP back on the map},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6835 LNCS},
	year = {2011},
	pages = {111{\textendash}121},
	abstract = {Conditional Random Fields (CRFs) are popular models in computer vision for solving labeling problems such as image denoising. This paper tackles the rarely addressed but important problem of learning the full form of the potential functions of pairwise CRFs. We examine two popular learning techniques, maximum likelihood estimation and maximum margin training. The main focus of the paper is on models such as pairwise CRFs, that are simplistic (misspecified) and do not fit the data well. We empirically demonstrate that for misspecified models maximum-margin training with MAP prediction is superior to maximum likelihood estimation with any other prediction method. Additionally we examine the common belief that MLE is better at producing predictions matching image statistics. {\textcopyright} 2011 Springer-Verlag.},
	isbn = {9783642231223},
	issn = {03029743},
	doi = {10.1007/978-3-642-23123-0_12},
	author = {Pletscher, Patrick and Nowozin, Sebastian and Kohli, Pushmeet and Carsten Rother}
}
@article {Nowozin2011,
	title = {Supplementary Material : Decision Tree Fields},
	journal = {Iccv},
	year = {2011},
	author = {Nowozin, Sebastian and Sharp, Toby}
}
@mastersthesis {nair_10_construction,
	title = {Construction and analysis of random tree ensembles},
	year = {2010},
	school = {University of Heidelberg},
	author = {Nair, R.}
}
@article {6395,
	title = {Learning an Interactive Segmentation System - Supplemental Material},
	year = {2010},
	author = {H. Nickisch and C. Rother and P. Kohli and C. Rhemann}
}
@mastersthesis {niegel2010,
	title = {Messung konvektionsgetriebener Transfergeschwindigkeit von Sauerstoff an der Luft-Wasser-Grenzfl{\"a}che},
	year = {2010},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Daniel Niegel}
}
@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}
}
@conference {Glocker2010,
	title = {TriangleFlow: Optical flow with triangulation-based higher-order likelihoods},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6313 LNCS},
	number = {PART 3},
	year = {2010},
	pages = {272{\textendash}285},
	abstract = {We use a simple yet powerful higher-order conditional random field (CRF) to model optical flow. It consists of a standard photo-consistency cost and a prior on affine motions both modeled in terms of higher-order potential functions. Reasoning jointly over a large set of unknown variables provides more reliable motion estimates and a robust matching criterion. One of the main contributions is that unlike previous region-based methods, we omit the assumption of constant flow. Instead, we consider local affine warps whose likelihood energy can be computed exactly without approximations. This results in a tractable, so-called, higher-order likelihood function. We realize this idea by employing triangulation meshes which immensely reduce the complexity of the problem. Optimization is performed by hierarchical fusion moves and an adaptive mesh refinement strategy. Experiments show that we achieve high-quality motion fields on several data sets including the Middlebury optical flow database. {\textcopyright} 2010 Springer-Verlag.},
	isbn = {364215557X},
	issn = {16113349},
	doi = {10.1007/978-3-642-15558-1_20},
	url = {http://vision.middlebury.edu/flow/},
	author = {Glocker, Ben and Heibel, T. Hauke and Navab, Nassir and Kohli, Pushmeet and Carsten Rother}
}
@incollection {beushausen2009,
	title = {2D-measurement technique for simultaneous quantitative determination of mixing ratio and velocity field in microfluidic applications},
	volume = {106},
	year = {2009},
	pages = {155--164},
	publisher = {Springer},
	abstract = {Two-dimensional Molecular-Tagging-Velocimetry (2D-MTV) has been used to investigate velocity fields of liquid flow in a micro mixer. Optical tagging was realized by using caged dye. For the first time patterns were generated by structured laser illumination using optical masks. This allows the generation of nearly any imaginable pattern. The flow induced deformation of the optically written pattern is tracked by imaging of laser induced fluorescence. Quantitative analysis of raw image series is carried out by novel optical flow based techniques. A comparison to the standard technique of uPIV has also been conducted. Additionally Planar Spontaneous Raman Scattering (PSRS) was applied in order to determine concentration fields for mixtures of ethanol and water.},
	doi = {10.1007/978-3-642-01106-1_16},
	author = {Volker Beushausen and Karsten Roetmann and Waldemar Schmunk and Mike Wellhausen and Christoph S. Garbe and Bernd J{\"a}hne},
	editor = {W. Nitsche and C. Dobriloff}
}
@incollection {Petra-et-al-09a,
	title = {3D Tomography from Few Projections in Experimental Fluid Mechanics},
	volume = {106},
	year = {2009},
	pages = {63-72},
	publisher = {Springer},
	author = {Stefania Petra and Schr{\"o}der, A. and Christoph Schn{\"o}rr},
	editor = {W. Nitsche and C. Dobriloff}
}
@mastersthesis {nagel2009,
	title = {Bestimmung der Temperaturdifferenz {\"u}ber die viskose Grenzschicht mit Hilfe des Oberfl{\"a}chenerneuerungsmodells},
	year = {2009},
	school = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Leila Nagel}
}
@conference {hoernlein2009,
	title = {Boosting shift-invariant features},
	booktitle = {Pattern Recognition},
	volume = {5748},
	year = {2009},
	pages = {121--130},
	publisher = {Springer},
	organization = {Springer},
	abstract = {This work presents a novel method for training shift-invariant features using a Boosting framework. Features performing local convolutions followed by subsampling are used to achieve shift-invariance. Other systems using this type of features, e.g. Convolutional Neural Networks, use complex feed-forward networks with multiple layers. In contrast, the proposed system adds features one at a time using smoothing spline base classifiers. Feature training optimizes base classifier costs. Boosting sample-reweighting ensures features to be both descriptive and independent. Our system has a lower number of design parameters as comparable systems, so adapting the system to new problems is simple. Also, the stage-wise training makes it very scalable. Experimental results show the competitiveness of our approach.},
	doi = {10.1007/978-3-642-03798-6_13},
	author = {Thomas H{\"o}rnlein and Bernd J{\"a}hne and Herbert S{\"u}{\ss}e},
	editor = {Joachim Denzler and Gunther Notni}
}
@conference {kaster_09_classification,
	title = {Classification of Spectroscopic Images in the DIROlab Environment},
	booktitle = {World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany},
	volume = {25/V},
	number = {25050252},
	year = {2009},
	pages = {252--255},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-03904-1_70},
	author = {F. O. Kaster and B. Michael Kelm and C. M. Zechmann and M.-A. Weber and Fred A. Hamprecht and Nix, O.},
	editor = {D{\"o}ssel, Olaf and Schlegel, Wolfgang C.}
}
@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 {nagel2009a,
	title = {First results from the 2009 SOPRAN active thermography pilot experiment in the Baltic Sea},
	booktitle = {Poster abstracts SOLAS Open Science Conference, Barcelona, 16--19 Sep. 2009},
	year = {2009},
	abstract = {To improve the parametrization of the transfer velocities a novel active thermography system was developed. At the end of SOPRAN 1 it has been deployed in a pilot experiment at the bow of the research vessel FS Alkor in the Baltic sea. A periodically varying heat flux is applied by a CO2-laser. The transfer velocity is estimated by the amplitude damping and phase shift of the temperature signal in the Fourier domain, gained from the infrared images of the water surface. First results are shown. The system will be used in the second phase of the SOPRAN project on a regular base. Together with eddy covariance measurements deployed from FINO 2, a significant improvement of the parametrization of the heat an gas transfer velocities can be expected.},
	author = {Leila Nagel and Uwe Schimpf and Bernd J{\"a}hne}
}
@article {thieke_09_framework,
	title = {A framework and multi-application prototype for integrated radiological diagnostics and radiation therapy},
	journal = {Strahlentherapie und Onkologie},
	volume = {185},
	number = {Suppl. 1},
	year = {2009},
	pages = {81},
	author = {Thieke, C. and Nix, O. and Koehn, A. and Floca, R. and van Straaten, D. and Hahn, H. and Strauss, L. G. and Siems, U. and Graf, M. and Pruem, H. and Klein, J. and Laue, H. and F. O. Kaster}
}
@techreport {popa09,
	title = {On a general extending and constraining procedure for linear iterative methods},
	year = {2009},
	institution = {IWR, University of Heidelberg},
	url = {http://www.ub.uni-heidelberg.de/archiv/9761},
	author = {Nicola, A. and Stefania Petra and Popa, C. and Christoph Schn{\"o}rr}
}
@techreport {popa09,
	title = {On a general extending and constraining procedure for linear iterative methods},
	year = {2009},
	month = {August},
	institution = {IWR, University of Heidelberg},
	type = {Technical Report},
	url = {http://www.ub.uni-heidelberg.de/archiv/9761},
	author = {Nicola, A. and Petra, S. and Popa, C. and Schn{\"o}rr, C.}
}
@conference {andres_09_quantitative,
	title = {Quantitative Assessment of Image Segmentation Quality by Random Walk Relaxation Times},
	booktitle = {Pattern Recognition. 31st DAGM Symposium, Jena, Germany, September 9-11, 2009. Proceedings},
	volume = {5748},
	year = {2009},
	note = {1},
	pages = {502-511},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-03798-6},
	author = {Bj{\"o}rn Andres and Ullrich K{\"o}the and Bonea, A. and Nadler, B. and Fred A. Hamprecht}
}
@incollection {garbe2009,
	title = {Spatiotemporal image analysis for flow measurements},
	volume = {106},
	year = {2009},
	pages = {289--305},
	publisher = {Springer},
	abstract = {In this chapter, a framework will be presented for measuring and modeling transport processes using novel visualization techniques and extended optical flow techniques for digital image sequence analysis. In this way, parameters besides the 2-D xy velocity components can be extracted concurrently from the acquired 2-D image sequences, such as wall shear rates and momentum transport close to boundaries, diffusion coefficients, and depth z in addition to the z velocity components. Depending on the application, particularly the temporal regularization can be enhanced, leading to stabilization of results and reduction of spatial regularization. This is frequently of high importance for flows close to boundaries. Results from applications will be presented from the fields of environmental and life sciences as well as from engineering.},
	doi = {10.1007/978-3-642-01106-1_29},
	author = {Christoph S. Garbe and Daniel Kondermann and Markus Jehle and Bernd J{\"a}hne},
	editor = {W. Nitsche and C. Dobriloff}
}
@conference {garbe2009b,
	title = {Thermographic measurements of the temperature difference across the air- water interface: results from experimental and numerical studies},
	booktitle = {Poster abstracts SOLAS Open Science Conference, Barcelona, 16--19 Sep. 2009},
	year = {2009},
	abstract = {Thermography is a powerful tool for analyzing spatially and temporally resolved the transport of heat across the air-water interface. In this contribution, measurements of the temperature difference across the aqueous boundary layer will be presented. These measurements are based on a statistical analysis of the temperature distribution directly at the water surface. This technique will be presented together with results of measurements conducted in the laboratory and in the field. These thermographic measurements are compared to measurements with standard techniques. Also, the same statistical analysis is performed on data from direct numerical simulation of a wind-driven, aqueous turbulent boundary-layer flow. The agreement between thermography and simulation is very good. Moreover, the full 3D simulations are used for a thorough analysis and validation of the thermographic technique and help to discuss the results.},
	author = {Christoph S. Garbe and Leila Nagel and Hung, L. -P. and Tsai, W. -T.}
}
@incollection {Vlasenko-Schnoerr-09a,
	title = {Variational Approaches for Model-Based PIV and Visual Fluid Analysis},
	volume = {106},
	year = {2009},
	pages = {247-256},
	publisher = {Springer},
	author = {Vlasenko, A. and Christoph Schn{\"o}rr},
	editor = {W. Nitsche and C. Dobriloff}
}
@incollection {Vlasenko-Schnoerr-09a,
	title = {Variational Approaches for Model-Based PIV and Visual Fluid Analysis},
	volume = {106},
	year = {2009},
	pages = {247-256},
	publisher = {Springer},
	author = {Vlasenko, A. and Schn{\"o}rr, C.},
	editor = {Nitsche, W. and C. Dobriloff}
}
@incollection {berthe2009,
	title = {The wall PIV measurement technique for near wall flow fields in biofliud mechanics},
	volume = {106},
	year = {2009},
	pages = {11--20},
	publisher = {Springer},
	abstract = {This chapter describes the development of a new time resolved 3D PIV technique for near wall flow field measurements. This measurement technique, called wall-PIV, is based on Beer-Lambert{\textquoteright}s law. It substitutes the classical PIV laser sheet by a diffuse, monochromatic full-field illumination that is limited to the near wall region by an absorbing molecular dye in the fluid. Aimed range of applications is the investigation of flow fields next to one- or two dimensionally curved, possibly flexing surfaces. The three dimensional three component flow estimation uses a new optical flow algorithm, based on particle trajectories. Results of the measurement technique{\textquoteright}s application on a displacement pediatric blood pump are presented.},
	doi = {10.1007/978-3-642-01106-1_2},
	author = {A. Berthe and Daniel Kondermann and Bernd J{\"a}hne and U. Ketzscher},
	editor = {W. Nitsche and C. Dobriloff}
}
@conference {Nguyen2009,
	title = {Weakly supervised discriminative localization and classification: A joint learning process},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	year = {2009},
	pages = {1925{\textendash}1932},
	abstract = {Visual categorization problems, such as object classification or action recognition, are increasingly often approached using a detection strategy: a classifier function is first applied to candidate subwindows of the image or the video, and then the maximum classifier score is used for class decision. Traditionally, the subwindow classifiers are trained on a large collection of examples manually annotated with masks or bounding boxes. The reliance on time-consuming human labeling effectively limits the application of these methods to problems involving very few categories. Furthermore, the human selection of the masks introduces arbitrary biases (e.g. in terms of window size and location) which may be suboptimal for classification. In this paper we propose a novel method for learning a discriminative subwindow classifier from examples annotated with binary labels indicating the presence of an object or action of interest, but not its location. During training, our approach simultaneously localizes the instances of the positive class and learns a subwindow SVM to recognize them. We extend our method to classification of time series by presenting an algorithm that localizes the most discriminative set of temporal segments in the signal. We evaluate our approach on several datasets for object and action recognition and show that it achieves results similar and in many cases superior to those obtained with full supervision. {\textcopyright}2009 IEEE.},
	isbn = {9781424444205},
	doi = {10.1109/ICCV.2009.5459426},
	author = {Nguyen, Minh Hoai and Torresani, Lorenzo and De La Torre, Fernando and Carsten Rother}
}
@conference {Nguyen2009a,
	title = {Weakly supervised discriminative localization and classification: A joint learning process},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	year = {2009},
	pages = {1925{\textendash}1932},
	abstract = {Visual categorization problems, such as object classification or action recognition, are increasingly often approached using a detection strategy: a classifier function is first applied to candidate subwindows of the image or the video, and then the maximum classifier score is used for class decision. Traditionally, the subwindow classifiers are trained on a large collection of examples manually annotated with masks or bounding boxes. The reliance on time-consuming human labeling effectively limits the application of these methods to problems involving very few categories. Furthermore, the human selection of the masks introduces arbitrary biases (e.g. in terms of window size and location) which may be suboptimal for classification. In this paper we propose a novel method for learning a discriminative subwindow classifier from examples annotated with binary labels indicating the presence of an object or action of interest, but not its location. During training, our approach simultaneously localizes the instances of the positive class and learns a subwindow SVM to recognize them. We extend our method to classification of time series by presenting an algorithm that localizes the most discriminative set of temporal segments in the signal. We evaluate our approach on several datasets for object and action recognition and show that it achieves results similar and in many cases superior to those obtained with full supervision. {\textcopyright}2009 IEEE.},
	isbn = {9781424444205},
	doi = {10.1109/ICCV.2009.5459426},
	author = {Nguyen, Minh Hoai and Torresani, Lorenzo and De La Torre, Fernando and Carsten Rother}
}
@conference {Weber-et-al-06b,
	title = {A Benchmark Evaluation of Large-Scale Optimization Approaches to Binary Tomography},
	booktitle = {Discrete Geometry for Computer Imagery (DGCI 2006)},
	volume = {4245},
	year = {2006},
	pages = {146-156},
	publisher = {Springer},
	organization = {Springer},
	author = {Weber, S. and Nagy, A. and Sch{\"u}le, T. and Christoph Schn{\"o}rr and Kuba, A.}
}
@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.}
}
@conference {jaehne2005,
	title = {Air-Sea Gas Transfer; Schmidt Number dependency and intermittency},
	booktitle = {Presented at: International Liege Colloquium on Ocean Dynamics, Gas Transfer at Water Surfaces},
	year = {2005},
	author = {Bernd J{\"a}hne and Nielsen, R. and Christopher Popp and Uwe Schimpf and Christoph S. Garbe}
}
@incollection {Weber-et-al-05b,
	title = {Binary Tomography by Iterating Linear Programs},
	year = {2005},
	note = {(13 pages)},
	publisher = {Springer},
	author = {Weber, S. and Schn{\"o}rr, C. and Sch{\"u}le, T. and Hornegger, J.},
	editor = {Klette, R. and Kozera, R. and Noakes, L. and Weickert, J.}
}
@article {Neumann-et-al-ML,
	title = {Combined SVM-based Feature Selection and Classification},
	journal = {Machine Learning},
	volume = {61},
	year = {2005},
	pages = {129-150},
	author = {Neumann, J. and Schn{\"o}rr, C. and Steidl, G.}
}
@article {Neumann-et-al-PR,
	title = {Efficient Wavelet Adaption for Hybrid Wavelet-Large Margin Classifiers},
	journal = {Pattern Recognition},
	volume = {38},
	number = {11},
	year = {2005},
	pages = {1815-1830},
	author = {Neumann, J. and Schn{\"o}rr, C. and Steidl, G.}
}
@article {Ruhnau-et-al-05,
	title = {Variational Optical Flow Estimation for Particle Image Velocimetry},
	journal = {Experiments in Fluids},
	volume = {38},
	year = {2005},
	pages = {21--32},
	author = {Paul Ruhnau and Kohlberger, T. and Nobach, H. and Christoph Schn{\"o}rr}
}
@article {Ruhnau-et-al-05,
	title = {Variational Optical Flow Estimation for Particle Image Velocimetry},
	journal = {Experiments in Fluids},
	volume = {38},
	year = {2005},
	pages = {21{\textendash}32},
	author = {Ruhnau, P. and Kohlberger, T. and Nobach, H. and Schn{\"o}rr, C.}
}
@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}
}
@phdthesis {nielsen2004,
	title = {Gasaustausch - Entwicklung und Ergebnis eines schnellen Massenbilanzverfahrens zur Messung der Austauschparameter},
	year = {2004},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	abstract = {In this thesis a novel, fast and highly accurate technique was developed for measuring the transfer velocity of gases across the air-water interface. The method is based on a simultaneous measurement of concentrations of gas tracers, both on the air and water side. It is possible to induce very high mass fluxes across the interface by increasing the water sided concentration. This leads to a significant increase in the air sided concentration of the tracer, which can be measured within a couple of minutes. By conducting measurements in this scheme, transfer velocities are recovered 10 to 100 times faster than would be possible by measuring the water sided concentration alone. Due to this significant reduction in measurement time, the novel technique makes it possible to conduct several experiments directly one after the other. For the first time was it possible to carry out a series of measurements in one day. The relative accuracy of measuring transfer velocities was better than 1\%, which made it possible to determine the Schmidt number exponent precisely. The transition of the Schmidt number exponent from a flat to a rough water surface could be assessed for the first time. This allowed for a parametrization with the facet model. With respect to wind speed dependence a surprising result was achieved: The Schmidt number exponent transitions much slower than previously expected.},
	url = {http://www.ub.uni-heidelberg.de/archiv/5032},
	author = {Nielsen, R.}
}
@conference {Neumann-et-al-04a,
	title = {SVM-based Feature Selection by Direct Objective Minimisation},
	booktitle = {Pattern Recognition, Proc. 26th DAGM Symposium},
	series = {lncs},
	volume = {3175},
	year = {2004},
	pages = {212-219},
	publisher = {Springer},
	organization = {Springer},
	author = {Neumann, J. and Schn{\"o}rr, C. and Steidl, G.}
}
@incollection {Ruhnau-et-al-04,
	title = {Variational Optical Flow Estimation for Particle Image Velocimetry},
	year = {2004},
	month = {Sept},
	publisher = {Deutsche Gesellschaft f{\"u}r Laser-Anemometrie GALA e.V.},
	chapter = {30},
	address = {Karlsruhe},
	author = {Ruhnau, P. and Kohlberger, T. and Nobach, H. and Schn{\"o}rr, C.},
	editor = {Ruck, B. and Leder, A. and Dopheide}
}
@techreport {Neumann-et-al-04b,
	title = {Effectively Finding the Optimal Wavelet for Hybrid Wavelet - Large Margin Signal Classification},
	number = {5},
	year = {2003},
	note = {\emphPattern Recognition, revised and resubmitted},
	month = {March},
	institution = {Dept. Math. and Comp. Science},
	type = {Comp. Science Series, Technical Report},
	address = {University of Mannheim, Germany},
	author = {Neumann, J. and Schn{\"o}rr, C. and Steidl, G.}
}
@conference {Neumann-et-al-03a,
	title = {Feasible Adaption Criteria for Hybrid Wavelet {\textendash} Large Margin Classifiers},
	booktitle = {Proc. Computer Analysis of Images and Patterns (CAIP{\textquoteright}03)},
	series = {LNCS},
	volume = {2756},
	year = {2003},
	pages = {588{\textendash}595},
	publisher = {Springer},
	organization = {Springer},
	author = {Neumann, J. and Schn{\"o}rr, C. and Steidl, G.},
	editor = {Petkov, N. and Westenberg, M.A.}
}
@article {Keuchel-et-al-03a,
	title = {Automatic Land Cover Analysis for Tenerife by Supervised Classification using Remotely Sensed Data},
	journal = {Remote Sensing of Environment},
	year = {2002},
	note = {Subitted},
	author = {Keuchel, J. and Naumann, S. and Heiler, M. and Siegmund, A.}
}
@conference {Cremers-et-al-02a,
	title = {Nonlinear Shape Statistics in Mumford-Shah Based Segmentation},
	booktitle = {Computer Vision -- ECCV 2002)},
	volume = {2351},
	year = {2002},
	pages = {93--108},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	author = {Daniel Cremers and Kohlberger, Timo and Christoph Schn{\"o}rr},
	editor = {Heyden, A. and Sparr, G. and Johansen, P. and Nielsen, M.}
}
@conference {Cremers-et-al-02a,
	title = {Nonlinear Shape Statistics in Mumford-Shah Based Segmentation},
	booktitle = {Computer Vision {\textendash} ECCV 2002)},
	series = {lncs},
	volume = {2351},
	year = {2002},
	pages = {93{\textendash}108},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	author = {Daniel Cremers and Kohlberger, Timo and Christoph Schn{\"o}rr},
	editor = {Heyden, A. and Sparr, G. and Nielsen, M. and Johansen, P.}
}
@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 {spies2000c,
	title = {Dense structure estimation via regularised optical flow},
	booktitle = {VMV 2000},
	year = {2000},
	pages = {57--64},
	publisher = {Aka GmbH, Berlin},
	organization = {Aka GmbH, Berlin},
	author = {Hagen Spies and Norbert Kirchge{\ss}ner and Hanno Scharr and Bernd J{\"a}hne},
	editor = {Girod, G. and Greiner, G. and Hans-Peter Seidel and H. Niemann}
}
@conference {Cremers-et-al-00b,
	title = {Learning Translation Invariant Shape Knowledge for Steering Diffusion-Snakes},
	booktitle = {3rd Workshop on Dynamic Perception},
	series = {Proc. in Artificial Intelligence},
	volume = {9},
	year = {2000},
	pages = {117{\textendash}122},
	publisher = {Akad. Verlagsges.},
	organization = {Akad. Verlagsges.},
	address = {Berlin, Germany},
	author = {Daniel Cremers and Christoph Schn{\"o}rr and Weickert, Joachim and Schellewald, Christian},
	editor = {Baratoff, G. and Neumann, H.}
}
@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}
}
@conference {balschbach1998,
	title = {Multichannel shape from shading techniques for moving specular surfaces},
	booktitle = {ECCV 1998},
	volume = {1407},
	year = {1998},
	pages = {170--184},
	publisher = {Springer, Berlin},
	organization = {Springer, Berlin},
	doi = {10.1007/BFb0054740},
	author = {G{\"u}nther Balschbach and J. Klinke and Bernd J{\"a}hne},
	editor = {Burkhardt, H. and Neumann, B.}
}
@conference {Peckar-et-al-98a,
	title = {Non-Rigid Image Registration Using a Parameter-Free Elastic Model},
	booktitle = {9th British Machine Vision Conference (BMVC{\textquoteleft}98)},
	year = {1998},
	month = {Sept. 14{\textendash}17},
	pages = {134{\textendash}143},
	address = {Southampton/UK},
	author = {Peckar, W. and Schn{\"o}rr, C. and Rohr, K. and Stiehl, H.S.},
	editor = {Carter, J.N. and Nixon, M.S.}
}
@conference {hering1997,
	title = {A comprehensive study of algorithms for multidimensional flow field diagnostics},
	booktitle = {Proc. Optical 3D Measurement Techniques IV, Zurich, Sept. 29 - Oct. 2, 1997},
	year = {1997},
	pages = {436--443},
	publisher = {Wichmann},
	organization = {Wichmann},
	author = {Frank Hering and Horst Hau{\ss}ecker and Jochen Dieter and T. Netzsch and Bernd J{\"a}hne},
	editor = {A. Gr{\"u}n and H. Kahmen}
}
@conference {jaehne1997j,
	title = {The research unit (Forschergruppe) Image Sequence Processing to Study Dynamical Processes},
	booktitle = {Proc.\ 3D Image Analysis and Synthesis{\textquoteright}97, Erlangen (Germany), November 17--18, 1997},
	year = {1997},
	pages = {107--114},
	publisher = {infix},
	organization = {infix},
	author = {Bernd J{\"a}hne and Horst Hau{\ss}ecker and Ulrich Platt and Ulrich Schurr and Mark Stitt},
	editor = {Hans-Peter Seidel and B. Girod and H. Niemann}
}
@conference {haussecker1996a,
	title = {A tensor approach for local structure analysis in multi-dimensional images},
	booktitle = {3D Image Analysis and Synthesis},
	year = {1996},
	pages = {171--178},
	author = {Horst Hau{\ss}ecker and Bernd J{\"a}hne},
	editor = {Girod, G. and H. Niemann and Hans-Peter Seidel}
}
@article {Schnoerr-et-al-computing-96,
	title = {A Variational Approach to the Design of Early Vision Algorithms},
	journal = {Computing Suppl.},
	volume = {11},
	year = {1996},
	pages = {149-165},
	author = {Schn{\"o}rr, C. and Sprengel, R. and Neumann, B.}
}
@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}
}
@phdthesis {netsch1995,
	title = {Dreidimensionale Particle Tracking Velocimetry},
	year = {1995},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	url = {http://d-nb.info/945379838},
	author = {T. Netzsch}
}
@conference {netzsch1995,
	title = {A high-performance system for 3-dimensional particle tracking velocimetry in turbulent flow research using image sequences},
	booktitle = {Proc. ISPRS Intercommission workshop From Pixels to Sequences, Z{\"u}rich, March 22-24},
	volume = {30 Part 5W1},
	year = {1995},
	pages = {202--207},
	publisher = {Risc Books},
	organization = {Risc Books},
	doi = {10.5281/zenodo.14892},
	author = {T. Netzsch 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 {Sprengel-Schnoerr-dagm-94,
	title = {Detection of Visual Data Transitions in Nonlinear Parameter Space},
	booktitle = {Mustererkennung 1994},
	series = {Informatik Xpress},
	volume = {5},
	year = {1994},
	month = {September},
	pages = {315{\textendash}323},
	publisher = {Technische Universit{\"a}t Wien},
	organization = {Technische Universit{\"a}t Wien},
	author = {Sprengel, R. and Schn{\"o}rr, C. and Neumann, B.},
	editor = {Kropatsch, W.G. and Bischof, H.}
}
@conference {Schnoerr-et-al-93,
	title = {Architekturkonzepte zur Bildauswertung},
	booktitle = {Grundlagen und Anwendungen der K{\"u}nstlichen Intelligenz, 17. Fachtagung f{\"u}r K{\"u}nstliche Intelligenz},
	year = {1993},
	pages = {268{\textendash}274},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Berlin},
	author = {Schn{\"o}rr, C. and Niemann, H. and Kopecz, J.},
	editor = {Herzog, O. and Christaller, T. and Sch{\"u}tt, D.}
}
@conference {netzsch1993,
	title = {Ein schnelles Verfahren zur L{\"o}sung des Stereokorrepondenzproblems bei der 3D-Particle Tracking Velocimetry},
	booktitle = {Proc. 15. DAGM-Symposium Mustererkennung},
	year = {1993},
	author = {T. Netzsch and Bernd J{\"a}hne}
}
@conference {netzsch1992,
	title = {Dreidimensionale Messung turbulenter Str{\"o}mung mit Bildverarbeitung},
	booktitle = {Proc. 14. DAGM-Symposium zur Mustererkennung},
	year = {1992},
	pages = {150--153},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-77785-1_18},
	author = {T. Netzsch and Bernd J{\"a}hne},
	editor = {Fuchs, S. and Hoffmann, R.}
}
@conference {Schnoerr-Neumann-dagm-92,
	title = {Ein Ansatz zur effizienten und eindeutigen Rekonstruktion st{\"u}ckweise glatter Funktionen},
	booktitle = {Mustererkennung 1992, 14. DAGM-Symposium},
	year = {1992},
	month = {sep},
	pages = {411{\textendash}416},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Dresden},
	author = {Schn{\"o}rr, C. and Neumann, B.},
	editor = {Fuchs, S. and Hoffmann, R.}
}
@conference {Schnoerr-dagm-89,
	title = {Zur Sch{\"a}tzung von Geschwindigkeitsvektorfeldern in Bildfolgen mit einer richtungsabh{\"a}ngigen Glattheitsforderung},
	booktitle = {Mustererkennung 1989, 11. DAGM-Symposium},
	series = {Informatik-Fachberichte},
	volume = {219},
	year = {1989},
	pages = {294{\textendash}301},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Hamburg},
	author = {Schn{\"o}rr, C.},
	editor = {Burkhardt, H. and H{\"o}hne, K.H. and Neumann, B.}
}
