Publications

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Author Title [ Type(Desc)] Year
Conference Paper
Diego, F and Hamprecht, F A (2013). Learning Multi-Level Sparse Representation for Identifying Neuronal Activity. Signal Processing with Adaptive Sparse Structured Representations Workshop (SPARS). Book of AbstractsPDF icon Technical Report (1.05 MB)
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
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
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
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--8PDF icon Technical Report (2.78 MB)
Afifi, M, Derpanis, K G, Ommer, B and Brown, M S (2020). Learning to Correct Overexposed and Underexposed Photos. https://arxiv.org/abs/2003.11596
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
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
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 419-427PDF icon Technical Report (2.87 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 268-275PDF icon Technical Report (2.92 MB)
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.2019.00036PDF icon PDF (8.94 MB)
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--194PDF icon Technical Report (358.98 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
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--521PDF icon Technical Report (984.89 KB)
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
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
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
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
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 (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
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
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
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
Fehr, J and Burkhardt, H (2009). Local Rotation Invariant Patch Descriptors for 3D Vector Fields. to be submitted
Bodnariuc, E, Petra, S, Schnörr, C and Voorneveld, J (2017). A Local Spatio-Temporal Approach to Plane Wave Ultrasound Particle Image Velocimetry. Proc. GCPR
Haja, A, Jähne, B and Abraham, S (2008). Localization accuracy of region detectors. Proceedings CVPR'08
Rathke, F, Desana, M and Schnörr, C (2017). Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans. Proc. MICCAI
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
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
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
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
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)PDF icon Article (8.75 MB)
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. 31-36PDF icon Technical Report (672.84 KB)

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