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2004
J. Keuchel, Heiler, M., and Schnörr, C., “Hierarchical Image Segmentation based on Semidefinite Programming”, in Pattern Recognition, Proc. 26th DAGM Symposium, 2004, vol. 3175, pp. 120-128.
S. Weber, Schüle, T., Schnörr, C., and Hornegger, J., “A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections”, Methods of Information in Medicine, vol. 43, pp. 320–326, 2004.
T. Kohlberger, Schnörr, C., Bruhn, A., and Weickert, J., “Parallel Variational Motion Estimation by Domain Decomposition and Cluster Computing”, in Computer Vision – ECCV 2004, 2004, vol. 3024, pp. 205-216.
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.
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.
2005
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.
S. Weber, Schnörr, C., Schüle, T., and Hornegger, J., “Binary Tomography by Iterating Linear Programs”, Geometric Properties from Incomplete Data. Springer, 2005.
J. Neumann, Schnörr, C., and Steidl, G., “Combined SVM-based Feature Selection and Classification”, Machine Learning, vol. 61, pp. 129-150, 2005.
J. Yuan, Ruhnau, P., Mémin, E., and Schnörr, C., “Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation”, in Scale-Space 2005, 2005, vol. 3459, pp. 267–278.
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.
M. Heiler and Schnörr, C., “Learning Sparse Image Codes by Convex Programming”, in Proc. Tenth IEEE Int. Conf. Computer Vision (ICCV'05), Beijing, China, 2005, pp. 1667-1674.
M. Heiler and Schnörr, C., “Natural Image Statistics for Natural Image Segmentation”, Int. J. Comp. Vision, vol. 63, pp. 5–19, 2005.
S. Weber, Schüle, T., and Schnörr, C., “Prior Learning and Convex-Concave Regularization of Binary Tomography”, Electr. Notes in Discr. Math., vol. 20, pp. 313-327, 2005.
C. Schellewald and Schnörr, C., “Probabilistic Subgraph Matching Based on Convex Relaxation”, in Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05), 2005, vol. 3757, pp. 171-186.
M. Heiler and Schnörr, C., “Reverse-Convex Programming for Sparse Image Codes”, in Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05), 2005, vol. 3757, pp. 600-616.
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.
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.
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.
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.
2006
M. Heiler and Schnörr, C., “Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming”, J. Mach. Learning Res., vol. 7, pp. 1385–1407, 2006.
P. Ruhnau, Stahl, A., and Schnörr, C., “On-Line Variational Estimation of Dynamical Fluid Flows with Physics-Based Spatio-Temporal Regularization”, in Proc. DAGM 2006, 2006, vol. 375-388, pp. 375-388.
2007
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.
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.
M. Welk, Weickert, J., Becker, F., Schnörr, C., Feddern, C., and Burgeth, B., “Median and related local filters for tensor-valued images”, Signal Processing, vol. 87, pp. 291-308, 2007.
P. Ruhnau and Schnörr, C., “Optical Stokes Flow Estimation: An Imaging-Based Control Approach”, Exp. in Fluids, vol. 42, pp. 61–78, 2007.
C. Schnörr, “Signal and Image Approximation with Level-Set Constraints”, Computing, vol. 81, pp. 137-160, 2007.
J. Yuan, Schnörr, C., and Steidl, G., “Simultaneous Optical Flow Estimation and Decomposition”, SIAM J. Scientific Computing, vol. 29, pp. 2283-2304, 2007.
S. Schmidt, Kappes, J. H., Bergtholdt, M., Pekar, V., Dries, S., Bystrov, D., and Schnörr, C., “Spine Detection and Labeling Using a Parts-Based Graphical Model”, in Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007), 2007, vol. 4584, pp. 122-133.
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.
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, Schüle, T., and Weber, S., “Variational Reconstruction with DC-Programming”, Advances in Discrete Tomography and Its Applications. Birkhäuser, Boston, 2007.
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), 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.

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