Publications

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Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Partial Optimality via Iterative Pruning for the Potts Model. Scale Space and Variational Methods (SSVM 2013)
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Partial Optimality via Iterative Pruning for the Potts Model. Scale Space and Variational Methods (SSVM 2013)PDF icon Technical Report (159.71 KB)
(2007). Pattern Recognition – 29th DAGM Symposium. LCNS. Springer. 4713
Schnörr, C and Jähne, B (2007). Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14. Springer. 4713
(2007). Pattern Recognition, 29Th Dagm Symposium, Heidelberg, Germany, September 12-14, 2007, Proceedings. Springer
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Persistency by Pruning for General Graphical Models. submitted to NIPS 2013
Denitiu, A, Petra, S, Schnörr, C and Schnörr, C (2014). Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections. Fundamenta Informaticae. 135 73–102
Denitiu, A, Petra, S, Schnörr, C and Schnörr, C (2014). Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections. Fundamenta Informaticae. 135 73--102PDF icon Technical Report (2.24 MB)
Vlasenko, A and Schnörr, C (2010). Physically Consistent and Efficient Variational Denoising of Image Fluid Flow Estimates. IEEE Trans.~Image Proc. 19 586-595PDF icon Technical Report (2.65 MB)
Vlasenko, A and Schnörr, C (2008). Physically Consistent Variational Denoising of Image Fluid Flow Estimates. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 406--415PDF icon Technical Report (1.6 MB)
Bodnariuc, E, Schiffner, M F, Petra, S and Schnörr, C (2016). Plane Wave Acoustic Superposition for Fast Ultrasound Imaging. International Ultrasonics Symposium
Kappes, J Hendrik, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2015). Probabilistic correlation clustering and image partitioning using perturbed Multicuts. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9087 231–242
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)
Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Med. Image Anal. 18 781–794
Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Medical Image Analysis. 18 781-794PDF icon Technical Report (4.07 MB)
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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)
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)
Berger, J, Lenzen, F, Becker, F, Neufeld, A and Schnörr, C (2015). Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. http://arxiv.org/abs/1507.06810
Berger, J, Lenzen, F, Becker, F, Neufeld, A and Schnörr, C (2015). Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. http://arxiv.org/abs/1507.06810PDF icon Technical Report (4.42 MB)
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
Görlitz, L, Menze, B H, Weber, M - A and Kelm, B Michael (2007). Semi-Supervised Tumor Detection in MRSI With Discriminative Random Fields. Pattern Recognition. Springer. 4713 224-233PDF icon Technical Report (872.46 KB)
Bergtholdt, M and Schnörr, C (2005). Shape Priors and Online Appearance Learning for Variational Segmentation and Object Recognition in Static Scenes. Pattern Recognition, Proc. 27th DAGM Symposium. Springer. 3663 342–350
Cremers, D, Kohlberger, T and Schnörr, C (2003). Shape Statistics in Kernel Space for Variational Image Segmentation. Pattern Recognition. 36 1929--1943PDF icon Technical Report (1.67 MB)
Cremers, D, Kohlberger, T and Schnörr, C (2003). Shape Statistics in Kernel Space for Variational Image Segmentation. Pattern Recognition. 36 1929–1943
Schnörr, (2007). Signal and Image Approximation with Level-Set Constraints. Computing. 81 137-160PDF icon Technical Report (506.8 KB)
Yuan, J, Schnörr, C and Steidl, G (2007). Simultaneous Optical Flow Estimation and Decomposition. SIAM J.~Scientific Computing. 29 2283-2304PDF icon Technical Report (1.16 MB)
Lenzen, F, Lellmann, J, Becker, F and Schnörr, C (2014). Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets. SIAM J.~Imag.~Sci. 7 2139--2174PDF icon Technical Report (802.13 KB)
Lenzen, F, Lellmann, J, Becker, F and Schnörr, C (2014). Solving QVIs for Image Restoration with Adaptive Constraint Sets. SIAM Journal on Imaging Sciences (SIIMS), in press
Breitenreicher, D, Lellmann, J and Schnörr, C (2011). Sparse Template-Based Variational Image Segmentation. Advances in Adaptive Data Analysis. 3 149-166PDF icon Technical Report (866.28 KB)
Breitenreicher, D, Lellmann, J and Schnörr, C (2011). Sparse Template-Based Variational Image Segmentation. Advances in Adaptive Data Analysis. 3 149-166
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)

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