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Parallel variational motion estimation by domain decomposition and cluster computing

T. Kohlberger, C. Schnörr, A. Bruhn, and J. Weickert. Proceedings of European Conference on Computer Vision (ECCV 2004). Lecture Notes in Computer Science, 3024, Springer, (2004)

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Christoph Schulz University of Stuttgart

Hon. -Prof. Christoph Müller University of Stuttgart

Dr. -Ing. Christoph Wenzel University of Stuttgart

Christoph Jeromin University of Stuttgart

Dr.-Ing. Christoph Steinhausen University of Stuttgart

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An efficient approach to the identification of characteristic intensity variations.K. Rohr, and C. Schnörr. Image Vision Comput., 11 (5): 273-277 (1993)Non-Rigid Image Registration Using a Parameter-Free Elastic Model.W. Peckar, C. Schnörr, K. Rohr, and H. Stiehl. BMVC, page 1-10. British Machine Vision Association, (1998)Efficient wavelet adaptation for hybrid wavelet-large margin classifiers.J. Neumann, C. Schnörr, and G. Steidl. Pattern Recognition, 38 (11): 1815-1830 (2005)Computation of discontinuous optical flow by domain decomposition and shape optimization.C. Schnörr. International Journal of Computer Vision, 8 (2): 153-165 (1992)Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets.F. Lenzen, J. Lellmann, F. Becker, and C. Schnörr. SIAM J. Imaging Sciences, 7 (4): 2139-2174 (2014)A general extending and constraining procedure for linear iterative methods.A. Nicola, S. Petra, C. Popa, and C. Schnörr. Int. J. Comput. Math., 89 (2): 231-253 (2012)High performance cluster computing with 3-D nonlinear diffusion filters.A. Bruhn, T. Jakob, M. Fischer, T. Kohlberger, J. Weickert, U. Brüning, and C. Schnörr. Real-Time Imaging, 10 (1): 41-51 (2004)Fast multivariate log-concave density estimation.F. Rathke, and C. Schnörr. Computational Statistics & Data Analysis, (2019)Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming.J. Keuchel, C. Schnörr, C. Schellewald, and D. Cremers. IEEE Trans. Pattern Anal. Mach. Intell., 25 (11): 1364-1379 (2003)Multicuts and Perturb & MAP for Probabilistic Graph Clustering.J. Kappes, P. Swoboda, B. Savchynskyy, T. Hazan, and C. Schnörr. CoRR, (2016)