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Conference Paper
S. Petra, Schnörr, C., Becker, F., and Lenzen, F., “B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2013, 2013, vol. 7893, pp. 110-124.PDF icon Technical Report (1.15 MB)
S. Petra, Schnörr, C., Becker, F., and Lenzen, F., “B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM, 2013, pp. 110-124.
A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A., “Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.
A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A., “Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.
T. Hodaň, 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., “BOP: Benchmark for 6D object pose estimation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11214 LNCS, pp. 19–35.
T. Hörnlein, Jähne, B., and Süße, H., “Boosting shift-invariant features”, in Pattern Recognition, 2009, vol. 5748, p. 121--130.
S. Weber, Schüle, T., Schnörr, C., and Kuba, A., “Binary Tomography with Deblurring”, in Combinatorial Image Analysis, 2006, vol. 4040, pp. 375-388.PDF icon Technical Report (803.63 KB)
S. Weber, Schüle, T., Hornegger, J., and Schnörr, C., “Binary Tomography by Iterating Linear Programs from Noisy Projections”, in Combinatorial Image Analysis, Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'04), 2004, vol. 3322, pp. 38–51.
B. Jähne, “Bildfolgenanalyse in der Umweltphysik: Wasseroberflächenwellen und Gasaustausch zwischen Atmosphäre und Gewässern”, in Proc. 8. DAGM-Symposium Mustererkennung 1986, 1986, p. 201--205.
K. Riemer, Scholz, T., and Jähne, B., “Bildfolgenanalyse im Orts-Wellenzahl-Raum”, in Proc. 13. DAGM-Symposium zur Mustererkennung 1991, München, 1991.
D. Wierzimok, Jähne, B., and Dengler, J., “Bildfolgenanalyse dreidimensionaler turbulenter Strömungen”, in Proc. 9. DAGM-Symposium zur Mustererkennung 1987, 1987, vol. 149, p. 288.
V. Kolmogorov, Criminisi, A., Blake, A., Cross, G., and Rother, C., “Bi-layer segmentation of binocular stereo video”, in Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, 2005, vol. II, pp. 407–414.
A. Monroy, Eigenstetter, A., and Ommer, B., “Beyond Straight Lines - Object Detection using Curvature”, in International Conference on Image Processing (ICIP), 2011.PDF icon Technical Report (2.65 MB)
C. Schnörr, “Bewegungssegmentation von Bildfolgen durch die Minimierung konvexer nicht-quadratischer Funktionale”, in Mustererkennung 1994, 1994, vol. 5, pp. 178–185.
M. Jehle, Klar, M., Köhler, H. - J., and Heibaum, M., “Bewegungsdetektion und Geschwindigkeitsanalyse in Bildfolgen zur Untersuchung von Sedimentverlagerungen”, in Mitteilungen des Instituts für Grundbau und Bodenmechanik, 2004, vol. 77, pp. 371-392.
C. Kamann and Rother, C., “Benchmarking the Robustness of Semantic Segmentation Models”, in CVPR 2020, 2020.PDF icon PDF (3.61 MB)
S. Weber, Nagy, A., Schüle, T., Schnörr, C., and Kuba, A., “A Benchmark Evaluation of Large-Scale Optimization Approaches to Binary Tomography”, in Discrete Geometry for Computer Imagery (DGCI 2006), 2006, vol. 4245, pp. 146-156.PDF icon Technical Report (301.1 KB)
M. Hissmann and Hamprecht, F. A., “Bayessche Schätzung von Höhenkarten aus der Wei\DF licht-Interferometrie”, in Oberflächenmesstechnik 2003, 2003, p. 187--196.
J. Giebel, Gavrila, D. M., and Schnörr, C., “A Bayesian Framework for Multi-cue 3D Object Tracking”, in Computer Vision – ECCV 2004, 2004, vol. 3024, pp. 241-252.
B. Michael Kelm, Müller, N., Menze, B. H., and Hamprecht, F. A., “Bayesian Estimation of Smooth Parameter Maps for Dynamic Contrast-Enhanced MR Images with Block-ICM”, in Proc Computer Vision and Pattern Recognition Workshop (Mathematical Methods in Biomedical Image Analysis), 2006, pp. 96-103.PDF icon Technical Report (232.69 KB)
P. Vincent Gehler, Rother, C., Blake, A., Minka, T., and Sharp, T., “Bayesian color constancy revisited”, in 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008.
S. T. Radev, Mertens, U. K., Voss, A., Ardizzone, L., and Köthe, U., “BayesFlow: Learning complex stochastic models with invertible neural networks”, 2020.PDF icon PDF (5.36 MB)
J. Fehr and Burkhardt, H., “A Bag of Features Approach for 3D Shape Retrieval”, in Proceedings of the ISVC 2009, Part I, 2009, vol. 5875, pp. 34-43.
B. Michael Kelm, Menze, B. H., and Hamprecht, F. A., “Automatische Lokalisation von Tumoren in 1H-NMR-spektroskopischen in vivo Aufnahmen”, in VDI-Berichte, 2005, vol. 1883, pp. 457-466.PDF icon Technical Report (221.54 KB)
D. Bister, Rohr, K., and Schnörr, C., “Automatische Bestimmung der Trajektorien von sich bewegenden Objekten aus einer Grauwertbildfolge”, in Mustererkennung 1990, 12. DAGM-Symposium, Oberkochen-Aalen, 1990, vol. 254, pp. 44–51.
D. Wierzimok and Jähne, B., “Automatic particle tracking velocimetry beneath a wind-stressed wavy water surface with image processing”, in 5th International Symposium on Flow Visualization, 1989.
D. Wierzimok and Jähne, B., “Automatic particle tracking beneath a wind-stressed wavy water surface with image processing”, in Proc.\ 5th Int. Symposium Flow Visualization, Praque 1989, 1990, p. 943--956.
A. Kreshuk, Straehle, C. N., Sommer, C., Köthe, U., Knott, G. W., and Hamprecht, F. A., “Automated Segmentation of Synapses in 3D EM Data”, in Eighth IEEE International Symposium on Biomedical Imaging (ISBI 2011). Proceedings, 2011, pp. 220-223.
M. Arnold, Bell, P., and Ommer, B., “Automated Learning of Self-Similarity and Informative Structures in Architecture”, in Scientific Computing & Cultural Heritage, 2013.
B. F. Tek, Kröger, T., Mikula, S., and Hamprecht, F. A., “Automated Cell Nucleus Detection for Large-Volume Electron Microscopy of Neural Tissue”, in ISBI. Proceedings, 2014, pp. 69-72.PDF icon Technical Report (533.92 KB)
H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented reality meets deep learning for car instance segmentation in urban scenes”, in British Machine Vision Conference 2017, BMVC 2017, 2017.
T. Kröger, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “Asymmetric Cuts: Joint Image Labeling and Partitioning”, in 36th German Conference on Pattern Recognition, 2014.
T. Kröger, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “Asymmetric Cuts: Joint Image Labeling and Partitioning”, in Pattern Recognition - 36th German Conference, {GCPR} 2014, Münster, Germany, September 2-5, 2014, Proceedings, 2014.PDF icon Technical Report (3.46 MB)
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “The Assignment Manifold: A Smooth Model for Image Labeling”, in Proc. 2nd Int. Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML'16; oral presentation; Grenander best paper award), 2016.
B. Güssefeld, Kondermann, D., Schwartz, C., and Klein, R., “Are reflectance field renderings appropriate for optical flow evaluation?”, in International Conference on Image Processing, ICIP 2014, 2014.

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