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Conference Paper

Aström, F, Hühnerbein, R, Savarino, F, Recknagel, J and Schnörr, C (2017). MAP Image Labeling Using Wasserstein Messages and Geometric Assignment. Proc. SSVM. Springer. 10302

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

Technical Report (672.84 KB)

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

Article (8.75 MB)

Pinggera, P, Ramos, S, Gehrig, S, Franke, U, Rother, C and Mester, R (2016). Lost and found: Detecting small road hazards for self-driving vehicles. IEEE International Conference on Intelligent Robots and Systems. 2016-Novem 1099–1106. http://www.6d-vision.com/lostandfounddataset

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

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

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

Schimpf, U, Nagel, L and Jähne, B (2011). Lock-in thermography at the ocean surface: a local and fast method to investigate heat and gas exchange between ocean and atmosphere. DPG Frühjahrstagung Dresden, Fachverband Umweltphysik. http://www.dpg-verhandlungen.de/year/2011/conference/dresden/part/up/session/1/contribution/28

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

Haja, A, Jähne, B and Abraham, S (2008). Localization accuracy of region detectors. Proceedings CVPR'08

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

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

Spies, H, Dierig, T, Garbe, C S and Würtz, R P (2002). Local models for dynamic processes in image sequences. Dynamic Perception. Aka GmbH. 59--64

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

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

Rother, C (2003). Linear multi-view reconstruction of points, lines, planes and cameras using a reference plane. Proceedings of the IEEE International Conference on Computer Vision. 2 1210–1217. http://www.nada.kth.se/carstenr

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

Rother, C and Carlsson, S (2001). Linear multi view reconstruction and camera recovery. Proceedings of the IEEE International Conference on Computer Vision. 1 42–49

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

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

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

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

Technical Report (984.89 KB)

Cremers, D, Schnörr, C, Weickert, J and Schellewald, C (2000). Learning Translation Invariant Shape Knowledge for Steering Diffusion-Snakes. 3rd Workshop on Dynamic Perception. Akad. Verlagsges., Berlin, Germany. 9 117–122

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

Technical Report (358.98 KB)

Leistner, T, Schilling, H, Mackowiak, R, Gumhold, S and Rother, C (2019). Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift. Proceedings - 2019 International Conference on 3D Vision, 3DV 2019. 249–257. http://arxiv.org/abs/1909.09059 http://dx.doi.org/10.1109/3DV.2019.00036

PDF (8.94 MB)

Funke, J, Hamprecht, F A and Zhang, C (2015). Learning to Segment: Training Hierarchical Segmentation under a Topological Loss. MICCAI. Proceedings, Part III. Springer. 9351 268-275

Technical Report (2.92 MB)

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

Technical Report (2.87 MB)

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

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

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

Technical Report (2.78 MB)

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

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

Jehle, M, Sommer, C and Jähne, B (2010). Learning of optimal illumination for material classification. Pattern Recognition. Springer. 6376 563--572

Bergtholdt, M, Kappes, J H and Schnörr, C (2006). Learning of Graphical Models and Efficient Inference for Object Class Recognition. Proc. DAGM 2006. Springer. 375-388 375-388

Afifi, M, Derpanis, K G, Ommer, B and Brown, M S (2021). Learning Multi-Scale Photo Exposure Correction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/abs/2003.11596

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