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D
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.
F. Aström and Schnörr, C., “Double-Opponent Vectorial Total Variation”, in Proc. ECCV, 2016.
K. Wiehler, Grigat, R. –R., Heers, J., Schnörr, C., and Stiehl, H. S., “Dynamic Circular Cellular Networks for Adaptive Smoothing of Multi–Dimensional Signals”, in Proc. 5th IEEE Int. Workshop on Cellular Neural Networks and their Applications, London, 1998.
E
J. Neumann, Schnörr, C., and Steidl, G., “Effectively Finding the Optimal Wavelet for Hybrid Wavelet - Large Margin Signal Classification”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 5, 2003.
M. Heiler, Cremers, D., and Schnörr, C., “Efficient Feature Subset Selection for Support Vector Machines”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 21/2001, 2001.
J. Neumann, Schnörr, C., and Steidl, G., “Efficient Wavelet Adaption for Hybrid Wavelet-Large Margin Classifiers”, Pattern Recognition, vol. 38, pp. 1815-1830, 2005.
C. Schnörr and Neumann, B., “Ein Ansatz zur effizienten und eindeutigen Rekonstruktion stückweise glatter Funktionen”, in Mustererkennung 1992, 14. DAGM-Symposium, Dresden, 1992, pp. 411–416.
S. Petra, Popa, C., and Schnörr, C., “Enhancing Sparsity by Constraining Strategies: Constrained SIRT versus Spectral Projected Gradient Methods”, in Proc. 7th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 08), Constanta, Romania, 2008.
S. Petra, Popa, C., and Schnörr, C., “Enhancing Sparsity by Constraining Strategies: Constrained SIRT versus Spectral Projected Gradient Methods”, in Proc. 7th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 08), Bucharest, Romania, 2008.
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.
C. Schellewald, Roth, S., and Schnörr, C., “Evaluation of Convex Optimization Techniques for the Weighted Graph–Matching Problem in Computer Vision”, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 361–368.
M. Desana and Schnörr, C., “Expectation Maximization for Sum-Product Networks as Exponential Family Mixture Models”. 2016.
F
J. Lellmann, Breitenreicher, D., and Schnörr, C., “Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision”, in European Conference on Computer Vision (ECCV), 2010, vol. 6312, pp. 494–505.
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.
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.
J. Neumann, Schnörr, C., and Steidl, G., “Feasible Adaption Criteria for Hybrid Wavelet – Large Margin Classifiers”, in Proc. Computer Analysis of Images and Patterns (CAIP'03), 2003, vol. 2756, pp. 588–595.
C. Schnörr, “On Functionals with Greyvalue-Controlled Smoothness Terms for Determining Optical Flow”, pami, vol. 15, pp. 1074–1079, 1993.
C. Schnörr, “Funktionalanalytische Methoden zur Bestimmung von Bewegungsinformation aus TV-Bildfolgen”. Fakultät für Informatik, Universität Karlsruhe (TH), 1991.
G
A. Nicola, Petra, S., Popa, C., and Schnörr, C., “On a general extending and constraining procedure for linear iterative methods”, IWR, University of Heidelberg, 2009.
A. Nicola, Petra, S., Popa, C., and Schnörr, C., “A general extending and constraining procedure for linear iterative methods”, Int. J. Comp. Math., 2011.
F. Aström and Schnörr, C., “A Geometric Approach for Color Image Regularization”, Comp. Vision Image Understanding, vol. 165, pp. 43–59, 2017.
F. Aström and Schnörr, C., “A Geometric Approach to Color Image Regularization”. 2016.
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “A Geometric Approach to Image Labeling”, in Proc. ECCV, 2016.
A. Zern, Rohr, K., and Schnörr, C., “Geometric Image Labeling with Global Convex Labeling Constraints”, in EMMCVPR, 2018, vol. 10746, pp. 533–547.
A. Zern, Rohr, K., and Schnörr, C., “Geometric Image Labeling with Global Convex Labeling Constraints”, in Proc. EMMCVPR, 2017.
A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, preprint: arXiv, 2018.
A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, Inverse Problems, vol. 36, p. 034004 (33pp), 2020.
A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, Inverse Problems, 2019.
C. Schnörr, Stiehl, H. - S., and Grigat, R. - R., “On Globally Asymptotically Stable Continuous-Time CNNs for Adaptive Smoothing of Multidimensional Signals”, in Proc. 4th IEEE Int. Workshop on Cellular Neural Networks and their Applications, Seville, Spain, 1996.
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”, J. Math. Imag. Vision, vol. 52, pp. 436–458, 2015.
L. Kostrykin, Schnörr, C., and Rohr, K., “Globally Optimal Segmentation of Cell Nuclei in Fluoroscence Microscopy Images using Shape and Intensity Information”, Medical Image Analysis, 2019.
J. Heers, Schnörr, C., and Stiehl, H. S., “Globally–Convergent Iterative Numerical Schemes for Non–Linear Variational Image Smoothing and Segmentation on a Multi–Processor Machine”, IEEE Trans. Image Proc., vol. 10, pp. 852–864, 2001.
M. Zisler, Savarino, F., Petra, S., and Schnörr, C., “Gradient Flows on a Riemannian Submanifold for Discrete Tomography”, in Proc. GCPR, 2017.
R. Karim, Bergtholdt, M., Kappes, J. H., and Schnörr, C., “Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification”, in Pattern Recognition – 29th DAGM Symposium, 2007, vol. 4713, pp. 395-404.

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