Publications

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Conference Paper
Kappes, J H, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2015). Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts. Proc.~SSVM. SpringerPDF icon Technical Report (1.1 MB)
Schellewald, C and Schnörr, C (2005). Probabilistic Subgraph Matching Based on Convex Relaxation. Proc.~Int.~Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05). Springer. 3757 171-186
Weickert, J and Schnörr, C (1999). Räumlich--zeitliche Berechnung des optischen Flusses mit nichtlinearen flussabhängigen Glattheitstermen. Mustererkennung 1999. Springer. 317--324
Wiehler, K, Grigat, R -- R, Heers, J, Schnörr, C and Stiehl, H S (1998). Real--Time Adaptive Smoothing with a 1D Nonlinear Relaxation Network in Analogue VLSI Technology. Mustererkennung 1998. Springer
Bruhn, A, Weickert, J, Feddern, C, Kohlberger, T and Schnörr, C (2003). Real-Time Optic Flow Computation with Variational Methods. Proc.~Computer Analysis of Images and Patterns (CAIP'03). Springer. 2756 222-229
Schnörr, (1996). Repräsentation von Bilddaten mit einem konvexen Variationsansatz. Mustererkennung 1996. Springer-Verlag. 21--28
Heiler, M and Schnörr, C (2005). Reverse-Convex Programming for Sparse Image Codes. Proc.~Int.~Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05). Springer. 3757 600-616
Berger, J, Neufeld, A, Becker, F, Lenzen, F and Schnörr, C (2015). Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations. Scale Space and Variational Methods in Computer Vision (SSVM 2015). Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-18461-6_32PDF icon Technical Report (364.01 KB)
Schnörr, (1994). Segmentation of Visual Motion by Minimizing Convex Non-Quadratic Functionals. 12th Int. Conf. on Pattern Recognition
Andres, B, Hamprecht, F A and Garbe, C S (2007). Selection of Local Optical Flow Models by Means of Residual Analysis. Pattern Recognition. Springer. 4713 72-81PDF icon Technical Report (229.64 KB)
Andres, B, Garbe, C S, Schnörr, C and Jähne, B (2007). Selection of local optical flow models by means of residual analysis. Proceedings of the 29th DAGM Symposium on Pattern Recognition. Springer. 72--81
Gerloff, S, Hagemann, A, Schnörr, C, Tieck, S, Stiehl, H S, Dombrowski, R, Dreyer, M and Wiesendanger, R (1997). Semi--Automated Analysis of SXM Images. Proc.~9th Int.~Conf.~on Scanning Tunneling Microscopy/Spectroscopy and Related Techniques (STM'97)
Görlitz, L, Menze, B H, Weber, M - A and Kelm, B M (2007). Semi-Supervised Tumor Detection in MRSI With Discriminative Random Fields. Pattern Recognition. Springer. 4713 224-233PDF icon Technical Report (872.46 KB)
Petra, S, Schröder, A, Wieneke, B and Schnörr, C (2008). On Sparsity Maximization in Tomographic Particle Image Reconstruction. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 294--303PDF icon Technical Report (1014.71 KB)
Lauer, F and Schnörr, C (2009). Spectral Clustering of Linear Subspaces for Motion Segmentation. Proc.~IEEE Int.~Conf.~Computer Vision (ICCV'09)PDF icon Technical Report (1.12 MB)
Lauer, F and Schnörr, C (2009). Spectral Clustering of Linear Subspaces for Motion Segmentation. Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan, in press. 678-685
Schmidt, S, Kappes, J H, Bergtholdt, M, Pekar, V, Dries, S, Bystrov, D and Schnörr, C (2007). Spine Detection and Labeling Using a Parts-Based Graphical Model. Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007). Springer. 4584 122-133PDF icon Technical Report (1.46 MB)
Savchynskyy, B, Kappes, J H, Schmidt, S and Schnörr, C (2011). A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)PDF icon Technical Report (408.99 KB)
Yuan, J, Schnörr, C, Steidl, G and Becker, F (2005). A Study of Non-Smooth Convex Flow Decomposition. Proc.~Variational, Geometric and Level Set Methods in Computer Vision. Springer. 3752 1--12
Schellewald, C and Schnörr, C (2003). Subgraph Matching with Semidefinite Programming. Proc.~Int.~Workshop on Combinatorial Image Analysis (IWCIA'03)
Neumann, J, Schnörr, C and Steidl, G (2004). SVM-based Feature Selection by Direct Objective Minimisation. Pattern Recognition, Proc.~26th DAGM Symposium. Springer. 3175 212-219
Kappes, J H, Petra, S, Schnörr, C and Zisler, M (2015). TomoGC: Binary Tomography by Constrained Graph Cuts. Proc.~GCPRPDF icon Technical Report (2.46 MB)
Petra, S, Schnörr, C, Schröder, A and Wieneke, B (2007). Tomographic Image Reconstruction in Experimental Fluid Dynamics: Synopsis and Problems. Proc.~6th Workshop on Modelling of Environmental and Life Sciences Problems (WMM~07). Ed Acad Romane, Bucuresti
Yuan, J, Schnörr, C and Steidl, G (2009). Total-Variation Based Piecewise Affine Regularization. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 552-564PDF icon Technical Report (478.04 KB)
Kappes, J H, Speth, M, Reinelt, G and Schnörr, C (2013). Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization. CVPRPDF icon Technical Report (623.84 KB)
Cremers, D, Sochen, N and Schnörr, C (2003). Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling. Scale Space Methods in Computer Vision. Springer. 2695 388--400PDF icon Technical Report (451.82 KB)
Peckar, W, Schnörr, C, Rohr, K and Stiehl, H S (1997). Two-Step Parameter-Free Elastic Image Registration with Prescribed Point Displacements. Proc.~9th Int.~Conf.~on Image Analysis and Processing (ICIAP'97)
Keuchel, J, Schnörr, C, Schellewald, C and Cremers, D (2002). Unsupervised Image Partitioning with Semidefinite Programming. Pattern Recognition, Proc.~24th DAGM Symposium. Springer. 2449 141--149
Schnörr, (2000). Variational Adaptive Smoothing and Segmentation. Computer Vision and Applications: A Guide for Students and Practitioners. Academic Press. 459--482
Becker, F, Wieneke, B, Yuan, J and Schnörr, C (2008). A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 335--344PDF icon Technical Report (1.82 MB)
Becker, F, Wieneke, B, Yuan, J and Schnörr, C (2008). A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry". Pattern Recognition -- 30th DAGM Symposium. 5096 335-344
Becker, F, Wieneke, B, Yuan, J and Schnörr, C (2008). Variational Correlation Approach to Flow Measurement with Window Adaption. 14th International Symposium on Applications of Laser Techniques to Fluid Mechanics. 1.1.3PDF icon Technical Report (3.37 MB)
Becker, F, Wieneke, B, Yuan, J and Schnörr, C (2008). Variational Correlation Approach to Flow Measurement with Window Adaption. 14th International Symposium on Applications of Laser Techniques to Fluid Mechanics. 1.1.8
Kohlberger, T, Mémin, E and Schnörr, C (2003). Variational Dense Motion Estimation Using the Helmholtz Decomposition. Scale Space Methods in Computer Vision. Springer. 2695 432--448
Lenzen, F, Becker, F, Lellmann, J, Petra, S and Schnörr, C (2011). Variational Image Denoising with Adaptive Constraint Sets. Proceedings of the 3nd International Conference on Scale Space and Variational Methods in Computer Vision 2011, in press. Springer. 6667 206-217

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