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Conference Paper

F. Aström, Hühnerbein, R., Savarino, F., Recknagel, J., and Schnörr, C., “MAP Image Labeling Using Wasserstein Messages and Geometric Assignment”, in Proc. SSVM, 2017, vol. 10302.

B. H. Menze, Kelm, B. Michael, Heck, D., Lichy, M. P., and Hamprecht, F. A., “Machine-based rejection of low quality spectra and estimation of brain tumor probabilities from magnetic resonance spectroscopic images”, in Bildverarbeitung für die Medizin, 2006, pp. 31-36.

Technical Report (672.84 KB)

B. Brattoli, Büchler, U., Wahl, A. - S., Schwab, M. E., and Ommer, B., “LSTM Self-Supervision for Detailed Behavior Analysis”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

Article (8.75 MB)

P. Pinggera, Ramos, S., Gehrig, S., Franke, U., Rother, C., and Mester, R., “Lost and found: Detecting small road hazards for self-driving vehicles”, in IEEE International Conference on Intelligent Robots and Systems, 2016, vol. 2016-Novem, pp. 1099–1106.

J. Jancsary, Nowozin, S., and Rother, C., “Loss-specific training of non-parametric image restoration models: A new state of the art”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7578 LNCS, pp. 112–125.

J. Jancsary, Nowozin, S., and Rother, C., “Loss-specific training of non-parametric image restoration models: A new state of the art”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7578 LNCS, pp. 112–125.

V. Lempitsky, Rother, C., and Blake, A., “LogCut - Efficient graph cut optimization for markov random fields”, in Proceedings of the IEEE International Conference on Computer Vision, 2007.

U. Schimpf, Nagel, L., and Jähne, B., “Lock-in thermography at the ocean surface: a local and fast method to investigate heat and gas exchange between ocean and atmosphere”, in DPG Frühjahrstagung Dresden, Fachverband Umweltphysik, 2011.

F. Rathke, Desana, M., and Schnörr, C., “Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans”, in Proc. MICCAI, 2017.

A. Haja, Jähne, B., and Abraham, S., “Localization accuracy of region detectors”, in Proceedings CVPR'08, 2008.

E. Bodnariuc, Petra, S., Schnörr, C., and Voorneveld, J., “A Local Spatio-Temporal Approach to Plane Wave Ultrasound Particle Image Velocimetry”, in Proc. GCPR, 2017.

J. Fehr and Burkhardt, H., “Local Rotation Invariant Patch Descriptors for 3D Vector Fields”, in to be submitted, 2009.

H. Spies, Dierig, T., Garbe, C. S., and Würtz, R. P., “Local models for dynamic processes in image sequences”, in Dynamic Perception, 2002, p. 59--64.

S. Weber, Schnörr, C., and Hornegger, J., “A Linear Programming Relaxation for Binary Tomography with Smoothness Priors”, in Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'03), Palermo, Italy, 2003.

S. Weber, Schüle, T., Schnörr, C., and Hornegger, J., “A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections”, in Bildverarbeitung für die Medizin 2003, 2003, pp. 41–45.

C. Rother, “Linear multi-view reconstruction of points, lines, planes and cameras using a reference plane”, in Proceedings of the IEEE International Conference on Computer Vision, 2003, vol. 2, pp. 1210–1217.

C. Rother and Carlsson, S., “Linear multi view reconstruction with missing data”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2002, vol. 2351, pp. 209–324.

C. Rother and Carlsson, S., “Linear multi view reconstruction and camera recovery”, in Proceedings of the IEEE International Conference on Computer Vision, 2001, vol. 1, pp. 42–49.

H. Scharr and Küsters, R., “A linear model for simultaneous estimation of 3D motion and depth”, in Proceedins of IEEE Workshop on Motion and Video Computing 2002, Orlando, 2002.

M. Diebold, Blum, O., Gutsche, M., Wanner, S., Garbe, C., Baker, H., and Jähne, B., “Light-field camera design for high-accuracy depth estimation”, in Videometrics, Range Imaging, and Applications XIII, 2015.

T. Münsterer and Jähne, B., “A LIF technique for the measurement of concentration profiles in the aqueous mass boundary layer”, in Proc.\ 7th Intern.\ Symp.\ on Appl.\ of Laser Techn.\ to Fluid Mechanics, Lisbon, Portugal, July 11.--14. 1994, 1994, vol. II, p. 29.4.1--5.

B. Antic, Milbich, T., and Ommer, B., “Less is More: Video Trimming for Action Recognition”, in Proceedings of the IEEE International Conference on Computer Vision, Workshop on Understanding Human Activities: Context and Interaction, 2013, p. 515--521.

Technical Report (984.89 KB)

D. Cremers, Schnörr, C., Weickert, J., and Schellewald, C., “Learning Translation Invariant Shape Knowledge for Steering Diffusion-Snakes”, in 3rd Workshop on Dynamic Perception, Berlin, Germany, 2000, vol. 9, pp. 117–122.

B. Ommer, Sauter, M., and M., B. J., “Learning Top-Down Grouping of Compositional Hierarchies for Recognition”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Perceptual Organization in Computer Vision, 2006, p. 194--194.

Technical Report (358.98 KB)

T. Leistner, Schilling, H., Mackowiak, R., Gumhold, S., and Rother, C., “Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift”, in Proceedings - 2019 International Conference on 3D Vision, 3DV 2019, 2019, pp. 249–257.

PDF (8.94 MB)

J. Funke, Hamprecht, F. A., and Zhang, C., “Learning to Segment: Training Hierarchical Segmentation under a Topological Loss”, in MICCAI. Proceedings, Part III, 2015, vol. 9351, pp. 268-275.

Technical Report (2.92 MB)

T. Kröger, Mikula, S., Denk, W., Köthe, U., and Hamprecht, F. A., “Learning to Segment Neurons with Non-local Quality Measures”, in MICCAI 2013. Proceedings, part II, 2013, vol. 8150, pp. 419-427.

Technical Report (2.87 MB)

J. Kruse, Rother, C., and Schmidt, U., “Learning to Push the Limits of Efficient FFT-Based Image Deconvolution”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 4596–4604.

O. Ghori, Mackowiak, R., Bautista, M., Beuter, N., Drumond, L., Diego, F., and Ommer, B., “Learning to Forecast Pedestrian Intention from Pose Dynamics”, in Intelligent Vehicles, IEEE, 2018, 2018.

B. Ommer and Buhmann, J. M., “Learning the Compositional Nature of Visual Objects”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007, p. 1--8.

Technical Report (2.78 MB)

M. Heiler and Schnörr, C., “Learning Sparse Image Codes by Convex Programming”, in Proc. Tenth IEEE Int. Conf. Computer Vision (ICCV'05), Beijing, China, 2005, pp. 1667-1674.

M. Jehle, Sommer, C., and Jähne, B., “Learning of Optimal Illumination for Material Classification”, in Proceedings of the 32nd DAGM Symposium on Pattern Recognition, Darmstadt, Germany, 2010, pp. 563-572.

M. Jehle, Sommer, C., and Jähne, B., “Learning of optimal illumination for material classification”, in Pattern Recognition, 2010, vol. 6376, p. 563--572.

M. Bergtholdt, Kappes, J. H., and Schnörr, C., “Learning of Graphical Models and Efficient Inference for Object Class Recognition”, in Proc. DAGM 2006, 2006, vol. 375-388, pp. 375-388.

M. Afifi, Derpanis, K. G., Ommer, B., and Brown, M. S., “Learning Multi-Scale Photo Exposure Correction”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

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