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Conference Paper
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, in 2011 IEEE International Conference on Computer Vision ICCV, 2011, pp. 1692-1699.
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, in 2011 IEEE International Conference on Computer Vision (ICCV), 2011, p. 1692 -- 1699.PDF icon Technical Report (4.9 MB)
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, in 2011 IEEE International Conference on Computer Vision (ICCV), 2011, pp. 1692 – 1699.
P. Swoboda and Schnörr, C., “Variational Image Segmentation and Cosegmentation with the Wasserstein Distance”, in Energy Minimization Methods in Computer Vision and Pattern Recognition, 2013, vol. 8081, p. 321--334.PDF icon Technical Report (8.06 MB)
P. Swoboda and Schnörr, C., “Variational Image Segmentation and Cosegmentation with the Wasserstein Distance”, in Energy Minimization Methods in Computer Vision and Pattern Recognition, 2013, vol. 8081, pp. 321–334.
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “Variational Image Denoising with Adaptive Constraint Sets”, in Proceedings of the 3nd International Conference on Scale Space and Variational Methods in Computer Vision 2011, in press, 2011, vol. 6667, pp. 206-217.
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “Variational Image Denoising with Adaptive Constraint Sets”, in LNCS, 2012, pp. 206-217.PDF icon Technical Report (649.03 KB)
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “Variational Image Denoising with Adaptive Constraint Sets”, in Proceedings of the 3rd International Conference on Scale Space and Variational Methods in Computer Vision 2011, 2012, pp. 206-217.
F. Becker, Wieneke, B., Yuan, J., and Schnörr, C., “Variational Correlation Approach to Flow Measurement with Window Adaption”, in 14th International Symposium on Applications of Laser Techniques to Fluid Mechanics, 2008, p. 1.1.8.
F. Becker, Wieneke, B., Yuan, J., and Schnörr, C., “Variational Correlation Approach to Flow Measurement with Window Adaption”, in 14th International Symposium on Applications of Laser Techniques to Fluid Mechanics, 2008, p. 1.1.3.PDF icon Technical Report (3.37 MB)
F. Becker, Wieneke, B., Yuan, J., and Schnörr, C., “Variational Correlation Approach to Flow Measurement with Window Adaption”, in 14th International Symposium on Applications of Laser Techniques to Fluid Mechanics, 2008, p. 1.1.3.
F. Becker, Wieneke, B., Yuan, J., and Schnörr, C., “A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry"”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, pp. 335-344.
F. Becker, Wieneke, B., Yuan, J., and Schnörr, C., “A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 335--344.PDF icon Technical Report (1.82 MB)
F. Becker, Wieneke, B., Yuan, J., and Schnörr, C., “A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry”, in Pattern Recognition – 30th DAGM Symposium, 2008, vol. 5096, pp. 335–344.
D. Cremers, Sochen, N., and Schnörr, C., “Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling”, in Scale Space Methods in Computer Vision, 2003, vol. 2695, p. 388--400.PDF icon Technical Report (451.82 KB)
D. Cremers, Sochen, N., and Schnörr, C., “Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling”, in Scale Space Methods in Computer Vision, 2003, vol. 2695, pp. 388–400.
J. H. Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization”, in CVPR, 2013.PDF icon Technical Report (623.84 KB)
J. H. Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization”, in CVPR, 2013.
J. Yuan, Schnörr, C., and Steidl, G., “Total-Variation Based Piecewise Affine Regularization”, in Scale Space and Variational Methods in Computer Vision (SSVM 2009), 2009, vol. 5567, pp. 552-564.PDF icon Technical Report (478.04 KB)
J. Yuan, Schnörr, C., and Steidl, G., “Total-Variation Based Piecewise Affine Regularization”, in Scale Space and Variational Methods in Computer Vision (SSVM 2009), 2009, vol. 5567, pp. 552-564.
J. H. Kappes, Petra, S., Schnörr, C., and Zisler, M., “TomoGC: Binary Tomography by Constrained Graph Cuts”, in Proc.~GCPR, 2015.PDF icon Technical Report (2.46 MB)
J. Yuan, Schnörr, C., Steidl, G., and Becker, F., “A Study of Non-Smooth Convex Flow Decomposition”, in Proc. Variational, Geometric and Level Set Methods in Computer Vision, 2005, vol. 3752, pp. 1–12.
B. Savchynskyy, Kappes, J. H., Schmidt, S., and Schnörr, C., “A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling”, in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2011.PDF icon Technical Report (408.99 KB)
S. Schmidt, Kappes, J. H., Bergtholdt, M., Pekar, V., Dries, S., Bystrov, D., and Schnörr, C., “Spine Detection and Labeling Using a Parts-Based Graphical Model”, in Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007), 2007, vol. 4584, pp. 122-133.PDF icon Technical Report (1.46 MB)
F. Lauer and Schnörr, C., “Spectral Clustering of Linear Subspaces for Motion Segmentation”, in Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan, in press, 2009, pp. 678-685.
F. Lauer and Schnörr, C., “Spectral Clustering of Linear Subspaces for Motion Segmentation”, in Proc.~IEEE Int.~Conf.~Computer Vision (ICCV'09), 2009.PDF icon Technical Report (1.12 MB)
S. Petra, Schröder, A., Wieneke, B., and Schnörr, C., “On Sparsity Maximization in Tomographic Particle Image Reconstruction”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 294--303.PDF icon Technical Report (1014.71 KB)
L. Görlitz, Menze, B. H., Weber, M. - A., and Kelm, B. Michael, “Semi-Supervised Tumor Detection in MRSI With Discriminative Random Fields”, in Pattern Recognition, 2007, vol. 4713, pp. 224-233.PDF icon Technical Report (872.46 KB)
B. Andres, Hamprecht, F. A., and Garbe, C. S., “Selection of Local Optical Flow Models by Means of Residual Analysis”, in Pattern Recognition, 2007, vol. 4713, pp. 72-81.PDF icon Technical Report (229.64 KB)
B. Andres, Garbe, C. S., Schnörr, C., and Jähne, B., “Selection of local optical flow models by means of residual analysis”, in Proceedings of the 29th DAGM Symposium on Pattern Recognition, 2007, p. 72--81.
J. Berger, Neufeld, A., Becker, F., Lenzen, F., and Schnörr, C., “Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations”, in Scale Space and Variational Methods in Computer Vision (SSVM 2015), 2015.PDF icon Technical Report (364.01 KB)
J. Berger, Neufeld, A., Becker, F., Lenzen, F., and Schnörr, C., “Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations”, in Scale Space and Variational Methods in Computer Vision (SSVM 2015), 2015.
A. Bruhn, Weickert, J., Feddern, C., Kohlberger, T., and Schnörr, C., “Real-Time Optic Flow Computation with Variational Methods”, in Proc. Computer Analysis of Images and Patterns (CAIP'03), 2003, vol. 2756, pp. 222-229.
J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc.~SSVM, 2015.PDF icon Technical Report (1.1 MB)
J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic correlation clustering and image partitioning using perturbed Multicuts”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9087, pp. 231–242.

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