Publications

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Conference Paper
Schellewald, C and Schnörr, C (2003). Subgraph Matching with Semidefinite Programming. Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'03). Palermo, Italy
Neumann, J, Schnörr, C and Steidl, G (2004). SVM-based Feature Selection by Direct Objective Minimisation. Pattern Recognition, Proc. 26th DAGM Symposium. Springer. 3175 212-219
Kappes, J H, Petra, S, Schnörr, C and Zisler, M (2015). TomoGC: Binary Tomography by Constrained Graph Cuts. Proc. GCPR
Petra, S, Schnörr, C, Schröder, A and Wieneke, B (2007). Tomographic Image Reconstruction in Experimental Fluid Dynamics: Synopsis and Problems. Proc. 6th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 07). Ed Acad Romane, Bucuresti, Constanta, Romania
Peckar, W, Schnörr, C, Rohr, K and Stiehl, H S (1997). Two-Step Parameter-Free Elastic Image Registration with Prescribed Point Displacements. Proc. 9th Int. Conf. on Image Analysis and Processing (ICIAP'97). Florence, Italy
Keuchel, J, Schnörr, C, Schellewald, C and Cremers, D (2002). Unsupervised Image Partitioning with Semidefinite Programming. Pattern Recognition, Proc. 24th DAGM Symposium. Springer, Zürich, Switzerland. 2449 141–149
Zern, A, Zisler, M, Aström, F, Petra, S and Schnörr, C (2018). Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment. GCPR
Zisler, M, Zern, A, Petra, S and Schnörr, C (2019). Unsupervised Labeling by Geometric and Spatially Regularized Self-Assignment. Proc. SSVM. Springer
Schnörr, (2000). Variational Adaptive Smoothing and Segmentation. Computer Vision and Applications: A Guide for Students and Practitioners. Academic Press, San Diego. 459–482
Kohlberger, T, Mémin, E and Schnörr, C (2003). Variational Dense Motion Estimation Using the Helmholtz Decomposition. Scale Space Methods in Computer Vision. Springer. 2695 432–448
Schnörr, C and Weickert, J (2000). Variational Image Motion Computation: Theoretical Framework, Problems and Perspectives. Mustererkennung 2000. Springer, Kiel, Germany
Schnörr, (1999). Variational Methods for Adaptive Image Smoothing and Segmentation. Handbook on Computer Vision and Applications: Signal Processing and Pattern Recognition. Academic Press, San Diego. 2 451–484
Savarino, F and Schnörr, C (2019). A Variational Perspective on the Assignment Flow. Proc. SSVM. Springer
Schnörr, (1989). Zur Schätzung von Geschwindigkeitsvektorfeldern in Bildfolgen mit einer richtungsabhängigen Glattheitsforderung. Mustererkennung 1989, 11. DAGM-Symposium. Springer-Verlag, Hamburg. 219 294–301
In Collection
Schnörr, (2020). Assignment Flows. Handbook of Variational Methods for Nonlinear Geometric Data. Springer. 235—260. https://www.springer.com/gp/book/9783030313500
Schnörr, (2019). Assignment Flows. Variational Methods for Nonlinear Geometric Data and Applications. Springer
Weber, S, Schnörr, C, Schüle, T and Hornegger, J (2005). Binary Tomography by Iterating Linear Programs. Geometric Properties from Incomplete Data. Springer
Heiler, M, Keuchel, J and Schnörr, C (2005). Semidefinite Clustering for Image Segmentation with A-priori Knowledge. Pattern Recognition, Proc. 27th DAGM Symposium. Springer. 3663 309–317
Vlasenko, A and Schnörr, C (2009). Variational Approaches for Model-Based PIV and Visual Fluid Analysis. Imaging Measurement Methods for Flow Analysis. Springer. 106 247-256
Ruhnau, P, Kohlberger, T, Nobach, H and Schnörr, C (2004). Variational Optical Flow Estimation for Particle Image Velocimetry. Proc. Lasermethoden in der Strömungsmeßtechnik. Deutsche Gesellschaft für Laser-Anemometrie GALA e.V., Karlsruhe
Schnörr, C, Schüle, T and Weber, S (2007). Variational Reconstruction with DC-Programming. Advances in Discrete Tomography and Its Applications. Birkhäuser, Boston
Journal Article
Wiehler, K, Heers, J, Schnörr, C, Stiehl, H –S and Grigat, R –R (2001). A 1D analog VLSI implementation for non-linear real-time signal preprocessing. Real–Time Imaging. 7 127–142
Schüle, T, Weber, S and Schnörr, C (2005). Adaptive Reconstruction of Discrete-Valued Objects from few Projections. Electr. Notes in Discr. Math. 20 365-384
Zern, A, Zeilmann, A and Schnörr, C (2020). Assignment Flows for Data Labeling on Graphs: Convergence and Stability. preprint: arXiv. https://arxiv.org/abs/2002.11571
Keuchel, J, Schnörr, C, Schellewald, C and Cremers, D (2003). Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming. 25 1364–1379
Neumann, J, Schnörr, C and Steidl, G (2005). Combined SVM-based Feature Selection and Classification. Machine Learning. 61 129-150
Schnörr, (1992). Computation of Discontinuous Optical Flow by Domain Decomposition and Shape Optimization. ijcv. 8 153–165
Lellmann, J and Schnörr, C (2011). Continuous Multiclass Labeling Approaches and Algorithms. CoRR. abs/1102.5448. http://arxiv.org/abs/1102.5448
Savarino, F and Schnörr, C (2019). Continuous-Domain Assignment Flows. preprint: arXiv. https://arxiv.org/abs/1910.07287
Schnörr, (1991). Determining Optical Flow for Irregular Domains by Minimizing Quadratic Functionals of a Certain Class. ijcv. 6 25–38
Schüle, T, Schnörr, C, Weber, S and Hornegger, J (2005). Discrete Tomography By Convex-Concave Regularization and D.C. Programming. Discr. Appl. Math. 151 229-243
Kohlberger, T, Schnörr, C, Bruhn, A and Weickert, J (2005). Domain decomposition for variational optical flow computation. IEEE Trans. Image Proc. 14 1125-1137
Neumann, J, Schnörr, C and Steidl, G (2005). Efficient Wavelet Adaption for Hybrid Wavelet-Large Margin Classifiers. Pattern Recognition. 38 1815-1830

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