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Publications

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Journal Article
C. Schnörr, “On Functionals with Greyvalue-Controlled Smoothness Terms for Determining Optical Flow”, pami, vol. 15, pp. 1074–1079, 1993.
J. Weickert, Heers, J., Schnörr, C., Zuiderveld, K. –J., Scherzer, O., and Stiehl, H. –S., “Fast parallel algorithms for a broad class of nonlinear variational diffusion approaches”, Real–Time Imaging, vol. 7, pp. 31–45, 2001.
F. Rathke and Schnörr, C., “Fast Multivariate Log-Concave Density Estimation”, Comp. Statistics & Data Analysis, vol. 140, pp. 41–58, 2019.
F. Rathke and Schnörr, C., “Fast Multivariate Log-Concave Density Estimation”, preprint: arXiv, 2018.
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, pp. 1301–1314, 2007.
J. Neumann, Schnörr, C., and Steidl, G., “Efficient Wavelet Adaption for Hybrid Wavelet-Large Margin Classifiers”, Pattern Recognition, vol. 38, pp. 1815-1830, 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.
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.
C. Schnörr, “Determining Optical Flow for Irregular Domains by Minimizing Quadratic Functionals of a Certain Class”, ijcv, vol. 6, pp. 25–38, 1991.
F. Savarino and Schnörr, C., “Continuous-Domain Assignment Flows”, preprint: arXiv, 2019.
J. Lellmann and Schnörr, C., “Continuous Multiclass Labeling Approaches and Algorithms”, CoRR, vol. abs/1102.5448, 2011.
C. Schnörr, “Computation of Discontinuous Optical Flow by Domain Decomposition and Shape Optimization”, ijcv, vol. 8, pp. 153–165, 1992.
J. Neumann, Schnörr, C., and Steidl, G., “Combined SVM-based Feature Selection and Classification”, Machine Learning, vol. 61, pp. 129-150, 2005.
J. Keuchel, Schnörr, C., Schellewald, C., and Cremers, D., “Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming”, vol. 25, pp. 1364–1379, 2003.
A. Zern, Zeilmann, A., and Schnörr, C., “Assignment Flows for Data Labeling on Graphs: Convergence and Stability”, preprint: arXiv, 2020.
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.
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.
In Collection
C. Schnörr, Schüle, T., and Weber, S., “Variational Reconstruction with DC-Programming”, Advances in Discrete Tomography and Its Applications. Birkhäuser, Boston, 2007.
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.
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.
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.
S. Weber, Schnörr, C., Schüle, T., and Hornegger, J., “Binary Tomography by Iterating Linear Programs”, Geometric Properties from Incomplete Data. Springer, 2005.
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.
Conference Proceedings
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, Ed., “Künstliche Intelligenz: Special Issue on Medical Computer Vision”, vol. 3. 2000.
Conference Paper
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.
F. Savarino and Schnörr, C., “A Variational Perspective on the Assignment Flow”, in Proc. SSVM, 2019.
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.
C. Schnörr and Weickert, J., “Variational Image Motion Computation: Theoretical Framework, Problems and Perspectives”, in Mustererkennung 2000, Kiel, Germany, 2000.
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, “Variational Adaptive Smoothing and Segmentation”, in Computer Vision and Applications: A Guide for Students and Practitioners, San Diego, 2000, pp. 459–482.
M. Zisler, Zern, A., Petra, S., and Schnörr, C., “Unsupervised Labeling by Geometric and Spatially Regularized Self-Assignment”, in Proc. SSVM, 2019.
A. Zern, Zisler, M., Aström, F., Petra, S., and Schnörr, C., “Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment”, in GCPR, 2018.
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.

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