Publications

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Welk, M, Becker, F, Schnörr, C and Weickert, J (2005). Matrix-Valued Filters as Convex Programs. Scale-Space 2005. Springer. 3459 204--216
Welk, M, Weickert, J, Becker, F, Schnörr, C, Feddern, C and Burgeth, B (2007). Median and related local filters for tensor-valued images. Signal Processing. 87 291-308PDF icon Technical Report (1007.29 KB)
Wulf, M, Stiehl, H S and Schnörr, C (1999). A model of spatiotemporal receptive fields in the primate retina. Proc.~1st Göttingen Conf.~German Neurosci.~Soc.. II
Breitenreicher, D and Schnörr, C (2011). Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data. Int.~J.~Comp.~Vision. 92 32--52. http://www.springerlink.com/content/v266873267180602/PDF icon Technical Report (4.3 MB)
Wulf, M, Stiehl, H S and Schnörr, C (1999). Modeling spatiotemporal receptive fields in the primate retina. Proc.~Cognitive Neurosci.~Conf
Schmitzer, B and Schnörr, C (2013). Modelling convex shape priors and matching based on the Gromov-Wasserstein distance. Journal of Mathematical Imaging and Vision. 46 143-159PDF icon Technical Report (957.78 KB)
Cremers, D and Schnörr, C (2002). Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization. Pattern Recognition, Proc.~24th DAGM Symposium. Springer. 2449 472--480
Schnörr, C and Peckar, W (1995). Motion-Based Identification of Deformable Templates. Proc. 6th Int. Conf. on Computer Analysis of Images and Patterns (CAIP '95). Springer Verlag. 970 122-129
Kappes, J H, Schmidt, S and Schnörr, C (2010). MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation. European Conference on Computer Vision (ECCV). Springer. 6313 735--747
Kappes, J H, Schmidt, S and Schnörr, C (2010). MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation. European Conference on Computer Vision (ECCV). Springer Berlin / Heidelberg. 6313 735--747PDF icon Technical Report (1.49 MB)
Bruhn, A, Weickert, J, Kohlberger, T and Schnörr, C (2006). A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods. Int.~J.~Computer Vision. 70 257-277PDF icon Technical Report (447.65 KB)
Cremers, D, Sochen, N and Schnörr, C (2004). Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. Computer Vision -- ECCV 2004. Springer. 3024 74-86
Cremers, D, Sochen, N and Schnörr, C (2006). Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. IJCV. 66 67-81
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Heiler, M and Schnörr, C (2005). Natural Image Statistics for Natural Image Segmentation. Int.~J.~Comp.~Vision. 63 5--19
Heiler, M and Schnörr, C (2003). Natural Statistics for Natural Image Segmentation. Proc.~IEEE Int.~Conf.~Computer Vision (ICCV 2003). 1259-1266
Sprengel, R and Schnörr, C (1993). Nichtlineare Diffusion zur Integration visueller Daten - Anwendung auf Kernspintomogramme. Mustererkennung 1993, 15. DAGM-Symposium. Springer Verlag. 134--141
Schnörr, C and Sprengel, R (1994). A Nonlinear Regularization Approach to Early Vision. Biol. Cybernetics. 72 141--149
Cremers, D, Kohlberger, T and Schnörr, C (2002). Nonlinear Shape Statistics in Mumford-Shah Based Segmentation. Computer Vision -- ECCV 2002). Springer Verlag. 2351 93--108PDF icon Technical Report (636.58 KB)
Cremers, D, Kohlberger, T and Schnörr, C (2001). Nonlinear Shape Statistics via Kernel Spaces. Mustererkennung 2001. Springer. 2191 269--276PDF icon Technical Report (324.55 KB)
Peckar, W, Schnörr, C, Rohr, K and Stiehl, H S (1998). Non-Rigid Image Registration Using a Parameter-Free Elastic Model. 9th British Machine Vision Conference (BMVC`98). 134--143
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Schmitzer, B and Schnörr, C (2013). Object Segmentation by Shape Matching with Wasserstein Modes. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2013). 123-136
Ruhnau, P, Stahl, A and Schnörr, C (2006). On-Line Variational Estimation of Dynamical Fluid Flows with Physics-Based Spatio-Temporal Regularization. Proc.~DAGM 2006. Springer. 375-388 375-388PDF icon Technical Report (902.47 KB)
Becker, F, Petra, S and Schnörr, C (2014). Optical Flow. Handbook of Mathematical Methods in Imaging. Springer
Ruhnau, P and Schnörr, C (2007). Optical Stokes Flow Estimation: An Imaging-Based Control Approach. Exp.~in Fluids. 42 61--78PDF icon Technical Report (1.54 MB)
Lellmann, J, Lenzen, F and Schnörr, C (2013). Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. Journal of Mathematical Imaging and Vision. 47 (3) 239-257
Lellmann, J, Lenzen, F and Schnörr, C (2011). Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. Energy Min. Meth. Comp. Vis. Patt. Recogn. Springer. 132-146
Lellmann, J, Lenzen, F and Schnörr, C (2012). Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. Journal of Mathematical Imaging and Vision. Springer. 47 239-257PDF icon Technical Report (616.16 KB)
Lellmann, J, Lenzen, F and Schnörr, C (2011). Optimality Bounds For A Variational Relaxation Of The Image Partitioning Problem. IPA group, Heidelberg University. http://arxiv.org/abs/1112.0974
Lellmann, J, Lenzen, F and Schnörr, C (2011). Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. Energy Min. Meth. Comp. Vis. Patt. Recogn. Springer. 6819 132--146PDF icon Technical Report (1 MB)
Lellmann, J, Lenzen, F and Schnörr, C (2010). Optimality Bounds for Variational Relaxations of Optimal Partition Problems
Rathke, F, Schmidt, S and Schnörr, C (2011). Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography. MICCAI 2011, Proceedings. Springer. 6893 370-377
Rathke, F, Schmidt, S and Schnörr, C (2011). Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography. MICCAI. Springer. 6893 370--377PDF icon Technical Report (1.12 MB)
Rathke, F, Schmidt, S and Schnörr, C (2011). Order preserving and shape prior constrained intra-retinal layer segmentation in optical coherence tomography. Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011). Springer. 6893 370–377
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Kohlberger, T, Schnörr, C, Bruhn, A and Weickert, J (2004). Parallel Variational Motion Estimation by Domain Decomposition and Cluster Computing. Computer Vision -- ECCV 2004. Springer. 3024 205-216
Heers, J, Schnörr, C and Stiehl, H S (1998). Parallele und global konvergente iterative Minimierung nichtlinearer Variationsansätze zur adaptiven Glättung und Segmentation von Bildern. Mustererkennung 1998. Springer

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