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Publications

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Journal Article
C. Schnörr, “Signal and Image Approximation with Level-Set Constraints”, Computing, vol. 81, pp. 137-160, 2007.PDF icon Technical Report (506.8 KB)
D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, p. 1929--1943, 2003.PDF icon Technical Report (1.67 MB)
D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, pp. 1929–1943, 2003.
D. Breitenreicher and Schnörr, C., “Robust 3D object registration without explicit correspondence using geometric integration”, Machine Vision and Applications, vol. 21, pp. 601-611, 2010.
D. Breitenreicher and Schnörr, C., “Robust 3D object registration without explicit correspondence using geometric integration”, Machine Vision and Applications, vol. 21, pp. 601-611, 2010.PDF icon Technical Report (1.65 MB)
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. 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.
A. Vlasenko and Schnörr, C., “Physically Consistent and Efficient Variational Denoising of Image Fluid Flow Estimates”, IEEE Trans.~Image Proc., vol. 19, pp. 586-595, 2010.PDF icon Technical Report (2.65 MB)
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)
P. Swoboda, Shekhovtsov, A., Kappes, J. Hendrik, Schnörr, C., and Savchynskyy, B., “Partial Optimality by Pruning for MAP-Inference with General Graphical Models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, pp. 1370–1382, 2016.
J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for Variational Relaxations of Optimal Partition Problems”, 2010.
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, pp. 239-257, 2012.PDF icon Technical Report (616.16 KB)
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.
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, pp. 239-257, 2012.
P. Ruhnau and Schnörr, C., “Optical Stokes Flow Estimation: An Imaging-Based Control Approach”, Exp.~in Fluids, vol. 42, p. 61--78, 2007.PDF icon Technical Report (1.54 MB)
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.
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.PDF icon 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.
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)
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.
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.PDF icon Technical Report (4.3 MB)
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.PDF icon Technical Report (1007.29 KB)
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.
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.
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.PDF icon Technical Report (1.11 MB)
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., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”, J.~Math.~Imag.~Vision, vol. 52, p. 436--458, 2015.PDF icon Technical Report (1.97 MB)
A. Nicola, Petra, S., Popa, C., and Schnörr, C., “A general extending and constraining procedure for linear iterative methods”, Int.~J.~Comp.~Math., 2011.PDF icon Technical Report (633.79 KB)
C. Schellewald, Roth, S., and Schnörr, C., “Evaluation of a convex relaxation to a quadratic assignment matching approach for relational object views”, Image Vision Comp., vol. 25, p. 1301--1314, 2007.PDF icon Technical Report (439.9 KB)
B. Savchynskyy, Schmidt, S., Kappes, J. H., and Schnörr, C., “Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing”, UAI. Proceedings, pp. 746-755, 2012.
K. Rohr and Schnörr, C., “An Efficient Approach to the Identification of Characteristic Intensity Variations”, vol. 11, pp. 273–277, 1993.

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