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Techreport
S. Petra and Schnörr, C., “TomoPIV meets Compressed Sensing”, IWR, University of Heidelberg, 2009.
C. Schnörr, “Representation of Images by a Convex Variational Diffusion Approach”, FB Informatik, Universität Hamburg, FBI-HH-M-256/96, 1996.
C. Schellewald, Roth, S., and Schnörr, C., “Performance Evaluation of a Convex Relaxation Approach to the Quadratic Assignment of Relational Object Views”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 02/2002, 2002.
J. Heers, Schnörr, C., and Stiehl, H. S., “Investigating a class of iterative schemes and their parallel implementation for nonlinear variational image smoothing and segmentation”, Comp. Sci. Dept., AB KOGS, University of Hamburg, Germany, 283/99, 1999.
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.
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., “Effectively Finding the Optimal Wavelet for Hybrid Wavelet - Large Margin Signal Classification”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 5, 2003.
T. Kohlberger, Schnörr, C., Bruhn, A., and Weickert, J., “Domain Decomposition for Variational Optical Flow Computation”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 07/2003, 2003.
T. Schüle, Schnörr, C., Weber, S., and Hornegger, J., “Discrete Tomography By Convex-Concave Regularization and D.C. Programming”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 15, 2003.
J. Lellmann and Schnörr, C., “Continuous Multiclass Labeling Approaches and Algorithms”, Univ. of Heidelberg, 2010.
C. Schellewald, Roth, S., and Schnörr, C., “Application of convex optimization techniques to the relational matching of object views”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 2001.
PhD Thesis
C. Schnörr, “Variational approaches to Image Segmentation and Feature Extraction”. University of Hamburg, Comp. Sci. Dept., Hamburg, Germany, 1998.
C. Schnörr, “Funktionalanalytische Methoden zur Bestimmung von Bewegungsinformation aus TV-Bildfolgen”. Fakultät für Informatik, Universität Karlsruhe (TH), 1991.
Miscellaneous
F. Silvestri, Reinelt, G., and Schnörr, C., “Symmetry-free SDP Relaxations for Affine Subspace Clustering”. 2016.
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “Image Labeling by Assignment”. 2016.
R. Hühnerbein, Savarino, F., Aström, F., and Schnörr, C., “Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment”. 2017.
F. Aström and Schnörr, C., “A Geometric Approach to Color Image Regularization”. 2016.
M. Desana and Schnörr, C., “Expectation Maximization for Sum-Product Networks as Exponential Family Mixture Models”. 2016.
S. Petra, Schnörr, C., and Schröder, A., “Critical Parameter Values and Reconstruction Properties of Discrete Tomography: Application to Experimental Fluid Dynamics”. 2012.
Journal Article
P. Ruhnau, Kohlberger, T., Nobach, H., and Schnörr, C., “Variational Optical Flow Estimation for Particle Image Velocimetry”, Experiments in Fluids, vol. 38, pp. 21–32, 2005.
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.
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.
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.
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.
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.
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.
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.
C. Schnörr, “Unique Reconstruction of Piecewise Smooth Images by Minimizing Strictly Convex Non-Quadratic Functionals”, vol. 4, pp. 189–198, 1994.
S. Petra and Schnörr, C., “TomoPIV meets Compressed Sensing”, Pure Math. Appl., vol. 20, pp. 49 – 76, 2009.
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.
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.
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.
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.

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