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2014
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.PDF icon 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.
F. Becker, Petra, S., and Schnörr, C., “Optical Flow”, Handbook of Mathematical Methods in Imaging. Springer, 2014.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality by Pruning for MAP-inference with General GraphicalModels”, CVPR. Proceedings. pp. 1170-1177, 2014.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality by Pruning for MAP-inference with General Graphical Models”, in IEEE Conference on Computer Vision and Pattern Recognition 2014, 2014.PDF icon Technical Report (703.34 KB)
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality by Pruning for MAP-inference with General Graphical Models”, in IEEE Conference on Computer Vision and Pattern Recognition 2014, 2014.
A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections”, Fundamenta Informaticae, vol. 135, pp. 73–102, 2014.
A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections”, Fundamenta Informaticae, vol. 135, p. 73--102, 2014.PDF icon Technical Report (2.24 MB)
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Med. Image Anal., vol. 18, pp. 781–794, 2014.
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Medical Image Analysis, vol. 18, pp. 781-794, 2014.PDF icon Technical Report (4.07 MB)
F. Lenzen, Lellmann, J., Becker, F., and Schnörr, C., “Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets”, SIAM J.~Imag.~Sci., vol. 7, p. 2139--2174, 2014.PDF icon Technical Report (802.13 KB)
F. Lenzen, Lellmann, J., Becker, F., and Schnörr, C., “Solving QVIs for Image Restoration with Adaptive Constraint Sets”, SIAM Journal on Imaging Sciences (SIIMS), in press, 2014.
2013
S. Petra, Schnörr, C., Becker, F., and Lenzen, F., “B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM, 2013, pp. 110-124.
S. Petra, Schnörr, C., Becker, F., and Lenzen, F., “B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2013, 2013, vol. 7893, pp. 110-124.PDF icon Technical Report (1.15 MB)
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “A class of quasi-variational inequalities for adaptive image denoising and decomposition”, Computational Optimization and Applications, vol. 54, pp. 371-398, 2013.PDF icon Technical Report (748.66 KB)
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “A Class of Quasi-Variational Inequalities for Adaptive Image Denoising and Decomposition”, Computational Optimization and Applications (COAP), vol. 54 (2), pp. 371-398, 2013.
D. Breitenreicher, Lellmann, J., and Schnörr, C., “COAL: a generic modelling and prototyping framework for convex optimization problems of variational image analysis”, Optimization Methods and Software, vol. 28, pp. 1081-1094, 2013.PDF icon Technical Report (1.69 MB)
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Sungwoong, K., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C., “A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems”, in CVPR 2013. Proceedings, 2013.PDF icon Technical Report (1.35 MB)
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C., “A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem”, in CVPR, 2013.PDF icon Technical Report (1.35 MB)
B. Schmitzer and Schnörr, C., “Contour Manifolds and Optimal Transport”. 2013.
P. Swoboda and Schnörr, C., “Convex Variational Image Restoration with Histogram Priors”, SIAM J.~Imag.~Sci., vol. 6, pp. 1719-1735, 2013.PDF icon Technical Report (553.54 KB)
S. Petra, Schnörr, C., and Schröder, A., “Critical Parameter Values and Reconstruction Propertiesof Discrete Tomography: Application to Experimental FluidDynamics”, Fundamenta Informaticae, vol. 125, p. 285--312, 2013.PDF icon Technical Report (1.42 MB)
J. Lellmann, Lellmann, B., Widmann, F., and Schnörr, C., “Discrete and Continuous Models for Partitioning Problems”, Int.~J.~Comp.~Visionz, vol. 104, pp. 241-269, 2013.PDF icon Technical Report (4.74 MB)
B. Savchynskyy, Kappes, J. H., Swoboda, P., and Schnörr, C., “Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation”, in NIPS, 2013.PDF icon Technical Report (499.17 KB)
B. Savchynskyy, Kappes, J. H., Swoboda, P., and Schnörr, C., “Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation”, in NIPS. Proceedings, 2013, pp. 1950-1958.
B. Savchynskyy, Kappes, J. Hendrik, Swoboda, P., and Schnörr, C., “Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation”, in NIPS, 2013.
B. Schmitzer and Schnörr, C., “A Hierarchical Approach to Optimal Transport”, in Scale Space and Variational Methods (SSVM 2013), 2013, pp. 452-464.
J. Hendrik Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Higher-order Segmentation via Multicuts”, ArXiv e-prints. 2013.
J. H. Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Higher-order Segmentation via Multicuts”, ArXiv e-prints. 2013.PDF icon Technical Report (1.07 MB)
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.
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.PDF icon Technical Report (957.78 KB)
B. Schmitzer and Schnörr, C., “Object Segmentation by Shape Matching with Wasserstein Modes”, in Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2013), 2013, pp. 123-136.
J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, Journal of Mathematical Imaging and Vision, vol. 47 (3), pp. 239-257, 2013.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality via Iterative Pruning for the Potts Model”, in Scale Space and Variational Methods (SSVM 2013), 2013.PDF icon Technical Report (159.71 KB)
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality via Iterative Pruning for the Potts Model”, in Scale Space and Variational Methods (SSVM 2013), 2013.

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