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S

F. Lauer and Schnörr, C., “Spectral Clustering of Linear Subspaces for Motion Segmentation”, in Proc. IEEE Int. Conf. Computer Vision (ICCV'09), Kyoto, Japan, 2009.

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.

C. Schnörr, “A Study of a Convex Variational Diffusion Approach for Image Segmentation and Feature Extraction”, J. of Math. Imag. and Vision, vol. 8, pp. 271–292, 1998.

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.

C. Schellewald and Schnörr, C., “Subgraph Matching with Semidefinite Programming”, in Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'03), Palermo, Italy, 2003.

M. Desana and Schnörr, C., “Sum-Product Graphical Models”, Machine Learning, vol. 109, pp. 135–173, 2020.

M. Desana and Schnörr, C., “Sum-Product Graphical Models”, Machine Learning, 2019.

Y. Censor, Petra, S., and Schnörr, C., “Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case”, J. Appl. Numer. Optimization (in press; arXiv:1911.05498), vol. 2, pp. 15-62, 2020.

Y. Censor, Petra, S., and Schnörr, C., “Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case”, preprint: arXiv, 2019.

J. Neumann, Schnörr, C., and Steidl, G., “SVM-based Feature Selection by Direct Objective Minimisation”, in Pattern Recognition, Proc. 26th DAGM Symposium, 2004, vol. 3175, pp. 212-219.

F. Silvestri, Reinelt, G., and Schnörr, C., “Symmetry-free SDP Relaxations for Affine Subspace Clustering”. 2016.

T

J. Weickert and Schnörr, C., “A Theoretical Framework for Convex Regularizers in PDE–Based Computation of Image Motion”, Int. J. Computer Vision, vol. 45, pp. 245–264, 2001.

J. H. Kappes, Petra, S., Schnörr, C., and Zisler, M., “TomoGC: Binary Tomography by Constrained Graph Cuts”, in Proc. GCPR, 2015.

S. Petra, Schnörr, C., Schröder, A., and Wieneke, B., “Tomographic Image Reconstruction in Experimental Fluid Dynamics: Synopsis and Problems”, in Proc. 6th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 07), Constanta, Romania, 2007.

S. Petra and Schnörr, C., “TomoPIV meets Compressed Sensing”, IWR, University of Heidelberg, 2009.

S. Petra and Schnörr, C., “TomoPIV meets Compressed Sensing”, Pure Math. Appl., vol. 20, pp. 49 – 76, 2009.

W. Peckar, Schnörr, C., Rohr, K., and Stiehl, H. S., “Two-Step Parameter-Free Elastic Image Registration with Prescribed Point Displacements”, in Proc. 9th Int. Conf. on Image Analysis and Processing (ICIAP'97), Florence, Italy, 1997.

U

C. Schnörr, “Unique Reconstruction of Piecewise Smooth Images by Minimizing Strictly Convex Non-Quadratic Functionals”, vol. 4, pp. 189–198, 1994.

A. Zern, Zisler, M., Petra, S., and Schnörr, C., “Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment”, Journal of Mathematical Imaging and Vision, 2020.

A. Zern, Zisler, M., Petra, S., and Schnörr, C., “Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment”, preprint: arXiv, 2019.

J. Keuchel, Schnörr, C., Schellewald, C., and Cremers, D., “Unsupervised Image Partitioning with Semidefinite Programming”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 141–149.

A. Zern, Zisler, M., Aström, F., Petra, S., and Schnörr, C., “Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment”, in GCPR, 2018.

M. Zisler, Zern, A., Petra, S., and Schnörr, C., “Unsupervised Labeling by Geometric and Spatially Regularized Self-Assignment”, in Proc. SSVM, 2019.

V

C. Schnörr, “Variational Adaptive Smoothing and Segmentation”, in Computer Vision and Applications: A Guide for Students and Practitioners, San Diego, 2000, pp. 459–482.

P. Ruhnau, Gütter, C., Putze, T., and Schnörr, C., “A variational approach for particle tracking velocimetry”, Meas. Science and Techn., vol. 16, pp. 1449-1458, 2005.

C. Schnörr, Sprengel, R., and Neumann, B., “A Variational Approach to the Design of Early Vision Algorithms”, Computing Suppl., vol. 11, pp. 149-165, 1996.

A. Vlasenko and Schnörr, C., “Variational Approaches for Model-Based PIV and Visual Fluid Analysis”, Imaging Measurement Methods for Flow Analysis, vol. 106. Springer, pp. 247-256, 2009.

C. Schnörr, “Variational approaches to Image Segmentation and Feature Extraction”. University of Hamburg, Comp. Sci. Dept., Hamburg, Germany, 1998.

T. Kohlberger, Mémin, E., and Schnörr, C., “Variational Dense Motion Estimation Using the Helmholtz Decomposition”, in Scale Space Methods in Computer Vision, 2003, vol. 2695, pp. 432–448.

P. Ruhnau, Stahl, A., and Schnörr, C., “Variational Estimation of Experimental Fluid Flows with Physics-Based Spatio-Temporal Regularization”, Measurement Science and Technology, vol. 18, pp. 755-763, 2007.

D. Heitz, Mémin, E., and Schnörr, C., “Variational fluid flow measurements from image sequences: synopsis and perspectives”, Exp. Fluids, vol. 48, pp. 369-393, 2010.

N. Paragios, Faugeras, O., Chan, T., and Schnörr, C., Eds., “Variational, Geometric and Level Sets in Computer Vision (VLSM'05)”, lncs, vol. 3752. Springer, Beijing, China, 2005.

C. Schnörr and Weickert, J., “Variational Image Motion Computation: Theoretical Framework, Problems and Perspectives”, in Mustererkennung 2000, Kiel, Germany, 2000.

C. Schnörr, “Variational Methods for Adaptive Image Smoothing and Segmentation”, in Handbook on Computer Vision and Applications: Signal Processing and Pattern Recognition, San Diego, 1999, vol. 2, pp. 451–484.

J. Weickert and Schnörr, C., “Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint”, J. Math. Imaging and Vision, vol. 14, pp. 245–255, 2001.

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