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Schmund, D, Münsterer, T, Lauer, H, Jähne, B and Jähne, B (1995). The circular wind wave facilities at the University of Heidelberg. Air-Water Gas Transfer - Selected papers from the Third International Symposium on Air-Water Gas Transfer. AEON. 505--516

Hamprecht, F A, Thiel, W and van Gunsteren, W F (2002). Chemical library subset selection algorithms: a unified derivation using spatial statistics. Journal of Chemical Information and Computer Sciences. 42 414-428

Erz, M (2011). Charakterisierung von Laufzeitkamerasystemen für Lumineszenzlebensdauermessungen. IWR, Fakultät für Physik und Astronomie, Univ. Heidelberg. Dissertation. http://www.ub.uni-heidelberg.de/archiv/11598

Milbich, T, Roth, K, Sinha, S, Schmidt, L, Ghassemi, M and Ommer, B (2021). Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. https://arxiv.org/abs/2107.09562

Mackowiak, R, Lenz, P, Ghori, O, Diego, F, Lange, O and Rother, C (2019). CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation. British Machine Vision Conference 2018, BMVC 2018

Wolf, S (2016). Cell Tracking With Graphical Model Using Higher Order Features On Track Segments. University of Heidelberg

Kandemir, M and Hamprecht, F A (2015). Cell event detection in phase-contrast microscopy sequences from few annotations. MICCAI. Proceedings. Springer. LNCS 9351 316-323

Technical Report (564.69 KB)

Zhang, C, Yarkony, J and Hamprecht, F A (2014). Cell detection and segmentation using correlation clustering. MICCAI. Proceedings. Springer. 9-16

Technical Report (8.06 MB)

Welbl, J (2014). Casting Random Forests as Artificial Neural Networks (and Profiting from It). GCPR. Proceedings. 765-771

Technical Report (376.24 KB)

Straehle, C N, Köthe, U, Knott, G W and Hamprecht, F A (2011). Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images. MICCAI 2011, Proceedings. Springer. 6891 653-660

Technical Report (1.69 MB)

Kleesiek, J, Morshuis, J Nikolas, Isensee, F, Deike-Hofmann, K, Paech, D, Kickingereder, P, Köthe, U, Rother, C, Forsting, M, Wick, W, Bendszus, M, Schlemmer, H Peter and Radbruch, A (2019). Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study. Investigative Radiology. 54 653–660

Maier-Hein, L, Mersmann, S, Kondermann, D, Bodenstedt, S, Sanchez, A, Stock, C, Kenngott, H, Eisenmann, M and Speidel, S (2014). Can masses of non-experts train highly accurate image classifiers? A crowdsourcing approach to instrument segmentation in laparoscopic images. MICCAI

Zechmann, C M, Kelm, B Michael, Zamecnik, P, Ikinger, U, Waldherr, R, Röll, S, Delorme, S, Hamprecht, F A and Bachert, P (2006). Can man still beat the machine? Automated vs. manual pattern recognition of 3D MRSI data of prostate cancer patients. Proceedings of the 16th ISMRM

Technical Report (664.38 KB)

Mustikovela, S Karthik, Yang, M Ying and Rother, C (2016). Can ground truth label propagation from video help semantic segmentation?. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9915 LNCS 804–820

Mersmann, S, Seitel, A, Erz, M, Jähne, B, Nickel, F, Mieth, M, Mehrabi, A and Maier-Hein, L (2013). Calibration of time-of-flight cameras for accurate intraoperative surface reconstruction. Med. Phys. 40 082701

Rocholz, R (2010). Calibration Of The 2010-Cisg Setup At The Aeolotron. Institute of Environmental Physics, University of Heidelberg

Jähne, B and Schultz, H (1992). Calibration and accuracy of optical slope measurements for short wind waves. Optics of the Air-Sea Interface: Theory and Measurements. 1749 222--233

B

Kappes, J H, Savchynskyy, B and Schnörr, C (2012). A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation. CVPR. Proceedings. 1688-1695

Kappes, J H, Savchynskyy, B and Schnörr, C (2012). A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation. CVPR

Technical Report (430.63 KB)

Heikkonen, J, Koikkalainen, P and Schnörr, C (1994). Building Trajectories via Selforganization from Spatiotemporal Features. 12th Int. Conf. on Pattern Recognition. Jerusalem, Israel

Flothow, L (2017). Bubble Characteristics From Breaking Waves In Fresh Water And Simulated Seawater. Institut für Umweltphysik, Universität Heidelberg, Germany

Petra, S, Schnörr, C, Becker, F and Lenzen, F (2013). B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems. Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM. 110-124

Petra, S, Schnörr, C, Becker, F and Lenzen, F (2013). B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems. Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2013. Springer. 7893 110-124

Technical Report (1.15 MB)

Jähne, (2011). Bringing the ocean inside the lab, image processing in environmental sciences. www.laborundmore.de/

Lempitsky, V, Blake, A and Rother, C (2012). Branch-and-mincut: Global optimization for image segmentation with high-level priors. Journal of Mathematical Imaging and Vision. 44 315–329

Behl, A, Hosseini Jafari, O, Mustikovela, S Karthik, Abu Alhaija, H, Rother, C and Geiger, A (2017). Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?. Proceedings of the IEEE International Conference on Computer Vision. 2017-Octob 2593–2602

Behl, A, Hosseini Jafari, O, Mustikovela, S Karthik, Abu Alhaija, H, Rother, C and Geiger, A (2017). Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?. Proceedings of the IEEE International Conference on Computer Vision. 2017-Octob 2593–2602

Hodaň, T, Michel, F, Brachmann, E, Kehl, W, Buch, A Glent, Kraft, D, Drost, B, Vidal, J, Ihrke, S, Zabulis, X, Sahin, C, Manhardt, F, Tombari, F, Kim, T Kyun, Matas, J and Rother, C (2018). BOP: Benchmark for 6D object pose estimation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11214 LNCS 19–35. http://arxiv.org/abs/1808.08319

Hörnlein, T, Jähne, B and Süße, H (2009). Boosting shift-invariant features. Pattern Recognition. Springer. 5748 121--130

Hörnlein, (2010). Boosted Feature Generation for Classification Problems Involving High Numbers of Inputs and Classes. IWR, Fakultät für Mathematik und Informatik, Univ. Heidelberg. Dissertation

Bendinger, A L, Debus, C, Glowa, C, Karger, C P, Peter, J and Storath, M (2019). Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models, in press. Physics in Medicine and Biology. 64

Jähne, (1999). Blätter, Wind und Wellen. Unsichtbares wird sichtbar.. computer art faszination, dot'99. dot-Verlag. 38--43

Scholes, M C, Matrai, P A, Andreae, M O, Smith, K A, Manning, M R, Artaxo, P, Barrie, L A, Bates, T S, Butler, J H, Ciccioli, P, Cieslik, S A, Delmas, R J and Dentener, F J (2003). Biosphere-Atmosphere Interactions. Atmospheric Chemistry in a Changing World, An Integration and Synthesis of a Decade of Tropospheric Chemistry Research. Springer. 19--71

Lou, X (2011). Biomedical Data Analysis with Prior Knowledge: Modeling and Learning. University of Heidelberg

Ozlu, N, Monigatti, F, Renard, B Y, Field, C M, Steen, H, Mitchison, T J and Steen, J J (2009). Binding partner switching on microtubules and aurora-B in the mitosis to cytokinesis transition. Molecular & Cellular Proteomics

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