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Conference Paper
C. Schellewald and Schnörr, C., “Subgraph Matching with Semidefinite Programming”, in Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'03), Palermo, Italy, 2003.
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.
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.
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.
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.
C. Schnörr, “Variational Adaptive Smoothing and Segmentation”, in Computer Vision and Applications: A Guide for Students and Practitioners, San Diego, 2000, pp. 459–482.
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.
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.
F. Savarino and Schnörr, C., “A Variational Perspective on the Assignment Flow”, in Proc. SSVM, 2019.
C. Schnörr, “Zur Schätzung von Geschwindigkeitsvektorfeldern in Bildfolgen mit einer richtungsabhängigen Glattheitsforderung”, in Mustererkennung 1989, 11. DAGM-Symposium, Hamburg, 1989, vol. 219, pp. 294–301.
Conference Proceedings
C. Schnörr, Ed., “Künstliche Intelligenz: Special Issue on Medical Computer Vision”, vol. 3. 2000.
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.
In Collection
C. Schnörr, “Assignment Flows”, Handbook of Variational Methods for Nonlinear Geometric Data. Springer, p. 235—260, 2020.
C. Schnörr, “Assignment Flows”, Variational Methods for Nonlinear Geometric Data and Applications. Springer, 2019.
S. Weber, Schnörr, C., Schüle, T., and Hornegger, J., “Binary Tomography by Iterating Linear Programs”, Geometric Properties from Incomplete Data. Springer, 2005.
M. Heiler, Keuchel, J., and Schnörr, C., “Semidefinite Clustering for Image Segmentation with A-priori Knowledge”, Pattern Recognition, Proc. 27th DAGM Symposium, vol. 3663. Springer, pp. 309–317, 2005.
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.
P. Ruhnau, Kohlberger, T., Nobach, H., and Schnörr, C., “Variational Optical Flow Estimation for Particle Image Velocimetry”, Proc. Lasermethoden in der Strömungsmeßtechnik. Deutsche Gesellschaft für Laser-Anemometrie GALA e.V., Karlsruhe, 2004.
C. Schnörr, Schüle, T., and Weber, S., “Variational Reconstruction with DC-Programming”, Advances in Discrete Tomography and Its Applications. Birkhäuser, Boston, 2007.
Journal Article
K. Wiehler, Heers, J., Schnörr, C., Stiehl, H. –S., and Grigat, R. –R., “A 1D analog VLSI implementation for non-linear real-time signal preprocessing”, Real–Time Imaging, vol. 7, pp. 127–142, 2001.
T. Schüle, Weber, S., and Schnörr, C., “Adaptive Reconstruction of Discrete-Valued Objects from few Projections”, Electr. Notes in Discr. Math., vol. 20, pp. 365-384, 2005.
A. Zern, Zeilmann, A., and Schnörr, C., “Assignment Flows for Data Labeling on Graphs: Convergence and Stability”, preprint: arXiv, 2020.
J. Keuchel, Schnörr, C., Schellewald, C., and Cremers, D., “Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming”, vol. 25, pp. 1364–1379, 2003.
J. Neumann, Schnörr, C., and Steidl, G., “Combined SVM-based Feature Selection and Classification”, Machine Learning, vol. 61, pp. 129-150, 2005.
C. Schnörr, “Computation of Discontinuous Optical Flow by Domain Decomposition and Shape Optimization”, ijcv, vol. 8, pp. 153–165, 1992.
J. Lellmann and Schnörr, C., “Continuous Multiclass Labeling Approaches and Algorithms”, CoRR, vol. abs/1102.5448, 2011.
F. Savarino and Schnörr, C., “Continuous-Domain Assignment Flows”, preprint: arXiv, 2019.
C. Schnörr, “Determining Optical Flow for Irregular Domains by Minimizing Quadratic Functionals of a Certain Class”, ijcv, vol. 6, pp. 25–38, 1991.
T. Schüle, Schnörr, C., Weber, S., and Hornegger, J., “Discrete Tomography By Convex-Concave Regularization and D.C. Programming”, Discr. Appl. Math., vol. 151, pp. 229-243, 2005.
T. Kohlberger, Schnörr, C., Bruhn, A., and Weickert, J., “Domain decomposition for variational optical flow computation”, IEEE Trans. Image Proc., vol. 14, pp. 1125-1137, 2005.
J. Neumann, Schnörr, C., and Steidl, G., “Efficient Wavelet Adaption for Hybrid Wavelet-Large Margin Classifiers”, Pattern Recognition, vol. 38, pp. 1815-1830, 2005.

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