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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)

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

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

Weber, S, Schüle, T, Schnörr, C and Kuba, A (2006). Binary Tomography with Deblurring. Combinatorial Image Analysis. Springer. 4040 375-388

Technical Report (803.63 KB)

Weber, S, Schüle, T, Hornegger, J and Schnörr, C (2004). Binary Tomography by Iterating Linear Programs from Noisy Projections. Combinatorial Image Analysis, Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'04). Springer Verlag. 3322 38–51

Jähne, (1986). Bildfolgenanalyse in der Umweltphysik: Wasseroberflächenwellen und Gasaustausch zwischen Atmosphäre und Gewässern. Proc. 8. DAGM-Symposium Mustererkennung 1986. 201--205. http://www.ub.uni-heidelberg.de/archiv/18103

Riemer, K, Scholz, T and Jähne, B (1991). Bildfolgenanalyse im Orts-Wellenzahl-Raum. Proc. 13. DAGM-Symposium zur Mustererkennung 1991, München

Wierzimok, D, Jähne, B and Dengler, J (1987). Bildfolgenanalyse dreidimensionaler turbulenter Strömungen. Proc. 9. DAGM-Symposium zur Mustererkennung 1987. Springer. 149 288

Kolmogorov, V, Criminisi, A, Blake, A, Cross, G and Rother, C (2005). Bi-layer segmentation of binocular stereo video. Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. II 407–414. http://research.microsoft.com/vision/cambridge

Monroy, A, Eigenstetter, A and Ommer, B (2011). Beyond Straight Lines - Object Detection using Curvature. International Conference on Image Processing (ICIP). IEEE

Technical Report (2.65 MB)

Schnörr, (1994). Bewegungssegmentation von Bildfolgen durch die Minimierung konvexer nicht-quadratischer Funktionale. Mustererkennung 1994. Technische Universität Wien. 5 178–185

Jehle, M, Klar, M, Köhler, H - J and Heibaum, M (2004). Bewegungsdetektion und Geschwindigkeitsanalyse in Bildfolgen zur Untersuchung von Sedimentverlagerungen. Mitteilungen des Instituts für Grundbau und Bodenmechanik. 77 371-392

Kamann, C and Rother, C (2020). Benchmarking the Robustness of Semantic Segmentation Models. CVPR 2020. http://arxiv.org/abs/1908.05005

PDF (3.61 MB)

Weber, S, Nagy, A, Schüle, T, Schnörr, C and Kuba, A (2006). A Benchmark Evaluation of Large-Scale Optimization Approaches to Binary Tomography. Discrete Geometry for Computer Imagery (DGCI 2006). Springer. 4245 146-156

Technical Report (301.1 KB)

Hissmann, M and Hamprecht, F A (2003). Bayessche Schätzung von Höhenkarten aus der Wei\DF licht-Interferometrie. Oberflächenmesstechnik 2003. 187--196

Giebel, J, Gavrila, D M and Schnörr, C (2004). A Bayesian Framework for Multi-cue 3D Object Tracking. Computer Vision – ECCV 2004. Springer. 3024 241-252

Kelm, B Michael, Müller, N, Menze, B H and Hamprecht, F A (2006). Bayesian Estimation of Smooth Parameter Maps for Dynamic Contrast-Enhanced MR Images with Block-ICM. Proc Computer Vision and Pattern Recognition Workshop (Mathematical Methods in Biomedical Image Analysis). IEEE Computer Society. 96-103

Technical Report (232.69 KB)

Gehler, P Vincent, Rother, C, Blake, A, Minka, T and Sharp, T (2008). Bayesian color constancy revisited. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Radev, S T, Mertens, U K, Voss, A, Ardizzone, L and Köthe, U (2020). BayesFlow: Learning complex stochastic models with invertible neural networks. http://arxiv.org/abs/2003.06281

PDF (5.36 MB)

Fehr, J and Burkhardt, H (2009). A Bag of Features Approach for 3D Shape Retrieval. Proceedings of the ISVC 2009, Part I. Springer. 5875 34-43

Kelm, B Michael, Menze, B H and Hamprecht, F A (2005). Automatische Lokalisation von Tumoren in 1H-NMR-spektroskopischen in vivo Aufnahmen. VDI-Berichte. 1883 457-466

Technical Report (221.54 KB)

Bister, D, Rohr, K and Schnörr, C (1990). Automatische Bestimmung der Trajektorien von sich bewegenden Objekten aus einer Grauwertbildfolge. Mustererkennung 1990, 12. DAGM-Symposium. Springer-Verlag, Oberkochen-Aalen. 254 44–51

Wierzimok, D and Jähne, B (1989). Automatic particle tracking velocimetry beneath a wind-stressed wavy water surface with image processing. 5th International Symposium on Flow Visualization

Wierzimok, D and Jähne, B (1990). Automatic particle tracking beneath a wind-stressed wavy water surface with image processing. Proc.\ 5th Int. Symposium Flow Visualization, Praque 1989. 943--956

Kreshuk, A, Straehle, C N, Sommer, C, Köthe, U, Knott, G W and Hamprecht, F A (2011). Automated Segmentation of Synapses in 3D EM Data. Eighth IEEE International Symposium on Biomedical Imaging (ISBI 2011). Proceedings. 220-223

Arnold, M, Bell, P and Ommer, B (2013). Automated Learning of Self-Similarity and Informative Structures in Architecture. Scientific Computing & Cultural Heritage

Tek, B F, Kröger, T, Mikula, S and Hamprecht, F A (2014). Automated Cell Nucleus Detection for Large-Volume Electron Microscopy of Neural Tissue. ISBI. Proceedings. 69-72

Technical Report (533.92 KB)

Abu Alhaija, H, Mustikovela, S Karthik, Mescheder, L, Geiger, A and Rother, C (2017). Augmented reality meets deep learning for car instance segmentation in urban scenes. British Machine Vision Conference 2017, BMVC 2017

Kröger, T, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2014). Asymmetric Cuts: Joint Image Labeling and Partitioning. 36th German Conference on Pattern Recognition

Kröger, T, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2014). Asymmetric Cuts: Joint Image Labeling and Partitioning. Pattern Recognition - 36th German Conference, {GCPR} 2014, Münster, Germany, September 2-5, 2014, Proceedings. http://dx.doi.org/10.1007/978-3-319-11752-2_16

Technical Report (3.46 MB)

Aström, F, Petra, S, Schmitzer, B and Schnörr, C (2016). The Assignment Manifold: A Smooth Model for Image Labeling. Proc. 2nd Int. Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML'16; oral presentation; Grenander best paper award)

Güssefeld, B, Kondermann, D, Schwartz, C and Klein, R (2014). Are reflectance field renderings appropriate for optical flow evaluation?. International Conference on Image Processing, ICIP 2014

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