Publications

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Bergtholdt, M, Kappes, J H, Schmidt, S and Schnörr, C (2010). A Study of Parts-Based Object Class Detection Using Complete Graphs. Int. J. Comp. Vision. 87 93-117. http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s11263-009-0209-1
Bergtholdt, M, Kappes, J H, Schmidt, S and Schnörr, C (2010). A Study of Parts-Based Object Class Detection Using Complete Graphs. Int.~J.~Comp.~Vision. 87 93-117. http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s11263-009-0209-1PDF icon Technical Report (2.18 MB)
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
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)
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), accepted as oral presentation. 1817 - 1823
Cremers, D and Schnörr, C (2003). Statistical Shape Knowledge in Variational Motion Segmentation. Image and Vision Comp. 21 77-86
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)
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
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)
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
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
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)
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)
Schnörr, (2007). Signal and Image Approximation with Level-Set Constraints. Computing. 81 137-160PDF icon Technical Report (506.8 KB)
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
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
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)
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
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)
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)

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