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N

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 269–276.

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision -- ECCV 2002), 2002, vol. 2351, p. 93--108.

Technical Report (636.58 KB)

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision – ECCV 2002), 2002, vol. 2351, pp. 93–108.

M. Zisler, Kappes, J. H., Schnörr, C., Petra, S., and Schnörr, C., “Non-Binary Discrete Tomography by Continuous Non-Convex Optimization”, IEEE Comp. Imaging, vol. 2, pp. 335-347, 2016.

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D. Cremers, Sochen, N., and Schnörr, C., “Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation”, in Computer Vision – ECCV 2004, 2004, vol. 3024, pp. 74-86.

D. Cremers, Sochen, N., and Schnörr, C., “Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation”, ijcv, vol. 66, pp. 67-81, 2006.

A. Bruhn, Weickert, J., Kohlberger, T., and Schnörr, C., “A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods”, Int.~J.~Computer Vision, vol. 70, pp. 257-277, 2006.

Technical Report (447.65 KB)

A. Bruhn, Weickert, J., Kohlberger, T., and Schnörr, C., “A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods”, Int. J. Computer Vision, vol. 70, pp. 257-277, 2006.

J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, Journal of Mathematical Imaging and Vision, vol. 56, pp. 221–237, 2016.

J. H. Kappes, Schmidt, S., and Schnörr, C., “MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation”, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, p. 735--747.

J. H. Kappes, Schmidt, S., and Schnörr, C., “MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation”, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, p. 735--747.

Technical Report (1.49 MB)

D. Cremers and Schnörr, C., “Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 472–480.

B. Schmitzer and Schnörr, C., “Modelling convex shape priors and matching based on the Gromov-Wasserstein distance”, Journal of Mathematical Imaging and Vision, vol. 46, pp. 143-159, 2013.

Technical Report (957.78 KB)

B. Schmitzer and Schnörr, C., “Modelling convex shape priors and matching based on the Gromov-Wasserstein distance”, Journal of Mathematical Imaging and Vision, vol. 46, pp. 143-159, 2013.

D. Breitenreicher and Schnörr, C., “Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data”, Int.~J.~Comp.~Vision, vol. 92, p. 32--52, 2011.

Technical Report (4.3 MB)

D. Breitenreicher and Schnörr, C., “Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data”, Int. J. Comp. Vision, vol. 92, pp. 32–52, 2011.

M. Welk, Weickert, J., Becker, F., Schnörr, C., Feddern, C., and Burgeth, B., “Median and related local filters for tensor-valued images”, Signal Processing, vol. 87, pp. 291-308, 2007.

Technical Report (1007.29 KB)

M. Welk, Becker, F., Schnörr, C., and Weickert, J., “Matrix-Valued Filters as Convex Programs”, in Scale-Space 2005, 2005, vol. 3459, pp. 204–216.

J. H. Kappes, Beier, T., and Schnörr, C., “MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves”, in Computer Vision - {ECCV} 2014 Workshops - Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part {II}, 2014.

Technical Report (557.49 KB)

J. Hendrik Kappes, Beier, T., and Schnörr, C., “MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves”, in International Workshop on Graphical Models in Computer Vision, 2014.

J. H. Kappes and Schnörr, C., “MAP-Inference for Highly-Connected Graphs with DC-Programming”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 1--10.

Technical Report (1.91 MB)

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A. Bruhn, Weickert, J., and Schnörr, C., “Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods”, vol. 61, pp. 211-231, 2005.

D. Cremers, Schnörr, C., Weickert, J., and Schellewald, C., “Learning Translation Invariant Shape Knowledge for Steering Diffusion-Snakes”, in 3rd Workshop on Dynamic Perception, Berlin, Germany, 2000, vol. 9, pp. 117–122.

M. Bergtholdt, Kappes, J. H., and Schnörr, C., “Learning of Graphical Models and Efficient Inference for Object Class Recognition”, in Proc. DAGM 2006, 2006, vol. 375-388, pp. 375-388.

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J. Berger and Schnörr, C., “Joint Recursive Monocular Filtering of Camera Motion and Disparity Map”, in 38th German Conference on Pattern Recognition, Hannover, 2016.

Technical Report (2.34 MB)

J. Berger and Schnörr, C., “Joint Recursive Monocular Filtering of Camera Motion and Disparity Map”, in 38th German Conference on Pattern Recognition, 2016.

I

D. Breitenreicher and Schnörr, C., “Intrinsic Second-Order Geometric Optimization for Robust Point Set Registration Without Correspondence”, in Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2009), 2009, vol. 5681, pp. 274-287.

Technical Report (752.29 KB)

D. Breitenreicher and Schnörr, C., “Intrinsic Second-Order Geometric Optimization for Robust Point Set Registration Without Correspondence”, in Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2009), 2009, vol. 5681, pp. 274-287.

J. Gall, Potthoff, J., Schnörr, C., Rosenhahn, B., and Seidel, H. - P., “Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications”, J.~Math.~Imag.~Vision, vol. 28, p. 1--18, 2007.

Technical Report (1.11 MB)

J. Gall, Potthoff, J., Schnörr, C., Rosenhahn, B., and Seidel, H. - P., “Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications”, J. Math. Imag. Vision, vol. 28, pp. 1–18, 2007.

H

J. H. Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Higher-order Segmentation via Multicuts”, ArXiv e-prints. 2013.

Technical Report (1.07 MB)

J. Hendrik Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Higher-order Segmentation via Multicuts”, ArXiv e-prints. 2013.

A. Bruhn, Jakob, T., Fischer, M., Weickert, J., Brüning, U., and Schnörr, C., “High performance cluster computing with 3-D nonlinear diffusion filters”, Real-Time Imaging, vol. 10, pp. 41–51, 2004.

B. Schmitzer and Schnörr, C., “A Hierarchical Approach to Optimal Transport”, in Scale Space and Variational Methods (SSVM 2013), 2013, pp. 452-464.

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R. Karim, Bergtholdt, M., Kappes, J. H., and Schnörr, C., “Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification”, in Pattern Recognition -- 29th DAGM Symposium, 2007, vol. 4713, pp. 395-404.

Technical Report (491.56 KB)

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