C.  Schnörr, ?Assignment Flows?, Handbook of Variational Methods for Nonlinear Geometric Data. Springer, p. 235?260, 2020.

A.  Zern, Zeilmann, A., and Schnörr, C., ?Assignment Flows for Data Labeling on Graphs: Convergence and Stability?, preprint: arXiv, 2020.

A.  Zeilmann, Savarino, F., Petra, S., and Schnörr, C., ?Geometric Numerical Integration of the Assignment Flow?, Inverse Problems, vol. 36, p. 034004 (33pp), 2020.

M.  Desana and Schnörr, C., ?Sum-Product Graphical Models?, Machine Learning, vol. 109, pp. 135?173, 2020.

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.

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, ?Assignment Flows?, Variational Methods for Nonlinear Geometric Data and Applications. Springer, 2019.

F.  Savarino and Schnörr, C., ?Continuous-Domain Assignment Flows?, preprint: arXiv, 2019.

F.  Rathke and Schnörr, C., ?Fast Multivariate Log-Concave Density Estimation?, Comp. Statistics & Data Analysis, vol. 140, pp. 41?58, 2019.

A.  Zeilmann, Savarino, F., Petra, S., and Schnörr, C., ?Geometric Numerical Integration of the Assignment Flow?, Inverse Problems, 2019.

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.

R.  Hühnerbein, Savarino, F., Petra, S., and Schnörr, C., ?Learning Adaptive Regularization for Image Labeling Using Geometric Assignment?, preprint: arXiv, 2019.

R.  Hühnerbein, Savarino, F., Petra, S., and Schnörr, C., ?Learning Adaptive Regularization for Image Labeling Using Geometric Assignment?, in Proc. SSVM, 2019.

M.  Zisler, Zern, A., Petra, S., and Schnörr, C., ?Self-Assignment Flows for Unsupervised Data Labeling on Graphs?, preprint: arXiv, 2019.

M.  Desana and Schnörr, C., ?Sum-Product Graphical Models?, Machine Learning, 2019.

Y.  Censor, Petra, S., and Schnörr, C., ?Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case?, 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?, preprint: arXiv, 2019.

M.  Zisler, Zern, A., Petra, S., and Schnörr, C., ?Unsupervised Labeling by Geometric and Spatially Regularized Self-Assignment?, in Proc. SSVM, 2019.

F.  Savarino and Schnörr, C., ?A Variational Perspective on the Assignment Flow?, in Proc. SSVM, 2019.

F.  Rathke and Schnörr, C., ?Fast Multivariate Log-Concave Density Estimation?, preprint: arXiv, 2018.

A.  Zern, Rohr, K., and Schnörr, C., ?Geometric Image Labeling with Global Convex Labeling Constraints?, in EMMCVPR, 2018, vol. 10746, pp. 533?547.

A.  Zeilmann, Savarino, F., Petra, S., and Schnörr, C., ?Geometric Numerical Integration of the Assignment Flow?, preprint: arXiv, 2018.

R.  Hühnerbein, Savarino, F., Aström, F., and Schnörr, C., ?Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment?, SIAM J. Imaging Science, vol. 11, pp. 1317?1362, 2018.

L.  Kostrykin, Schnörr, C., and Rohr, K., ?Segmentation of Cell Nuclei Using Intensity-Based Model Fitting and Sequential Convex Programming?, in Proc. ISBI, 2018.

A.  Zern, Zisler, M., Aström, F., Petra, S., and Schnörr, C., ?Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment?, in GCPR, 2018.

R.  Dalitz, Petra, S., and Schnörr, C., ?Compressed Motion Sensing?, in Proc. SSVM, 2017, vol. 10302.

F.  Aström and Schnörr, C., ?A Geometric Approach for Color Image Regularization?, Comp. Vision Image Understanding, vol. 165, pp. 43?59, 2017.

A.  Zern, Rohr, K., and Schnörr, C., ?Geometric Image Labeling with Global Convex Labeling Constraints?, in Proc. EMMCVPR, 2017.

M.  Zisler, Savarino, F., Petra, S., and Schnörr, C., ?Gradient Flows on a Riemannian Submanifold for Discrete Tomography?, in Proc. GCPR, 2017.

R.  Hühnerbein, Savarino, F., Aström, F., and Schnörr, C., ?Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment?. 2017.

F.  Aström, Petra, S., Schmitzer, B., and Schnörr, C., ?Image Labeling by Assignment?, J. Math. Imag. Vision, vol. 58, pp. 211?238, 2017.

M.  Zisler, Aström, F., Petra, S., and Schnörr, C., ?Image Reconstruction by Multilabel Propagation?, in Proc. SSVM, 2017, vol. 10302.

E.  Bodnariuc, Petra, S., Schnörr, C., and Voorneveld, J., ?A Local Spatio-Temporal Approach to Plane Wave Ultrasound Particle Image Velocimetry?, in Proc. GCPR, 2017.

F.  Rathke, Desana, M., and Schnörr, C., ?Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans?, in Proc. MICCAI, 2017.

F.  Aström, Hühnerbein, R., Savarino, F., Recknagel, J., and Schnörr, C., ?MAP Image Labeling Using Wasserstein Messages and Geometric Assignment?, in Proc. SSVM, 2017, vol. 10302.

F.  Savarino, Hühnerbein, R., Aström, F., Recknagel, J., and Schnörr, C., ?Numerical Integration of Riemannian Gradient Flows for Image Labeling?, in Proc. SSVM, 2017, vol. 10302.

J.  Berger, Lenzen, F., Becker, F., Neufeld, A., and Schnörr, C., ?{Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations?, J. Math. Imag. Vision, vol. 58, pp. 102?129, 2017.

P.  Markowsky, Reith, S., Zuber, T. E., König, R., Rohr, K., and Schnörr, C., ?Segmentation of cell structure using model-based set covering with iterative reweighting?, in Proc. ISBI, 2017.

F.  Aström, Petra, S., Schmitzer, B., and Schnörr, C., ?The Assignment Manifold: A Smooth Model for Image Labeling?, in Proc. 2nd Int. Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML'16; oral presentation; Grenander best paper award), 2016.

F.  Aström and Schnörr, C., ?Double-Opponent Vectorial Total Variation?, in Proc. ECCV, 2016.

M.  Desana and Schnörr, C., ?Expectation Maximization for Sum-Product Networks as Exponential Family Mixture Models?. 2016.

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.

J.  Kappes, Speth, M., Reinelt, G., and Schnörr, C., ?Higher-order Segmentation via Multicuts?, Comp. Vision Image Understanding, vol. 143, pp. 104?119, 2016.

F.  Aström, Petra, S., Schmitzer, B., and Schnörr, C., ?Image Labeling by Assignment?. 2016.

Y.  Censor, Gibali, A., Lenzen, F., and Schnörr, C., ?The Implicit Convex Feasibility Problem and Its Application to Adaptive Image Denoising?, J. Comp. Math., vol. 34, pp. 608-623, 2016.

J. H.  Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., ?Multicuts and Perturb & MAP for Probabilistic Graph Clustering?, J. Math. Imag. Vision, vol. 56, pp. 221?237, 2016.

E.  Bodnariuc, Petra, S., Poelma, C., and Schnörr, C., ?Parametric Dictionary-Based Velocimetry for Echo PIV?, in Proc. CGPR, 2016.

P.  Swoboda, Shekhovtsov, A., Kappes, J. H., Schnörr, C., and Savchynskyy, B., ?Partial Optimality by Pruning for MAP-Inference with General Graphical Models?, IEEE Trans. Patt. Anal. Mach. Intell., vol. 38, pp. 1370?1382, 2016.

F.  Silvestri, Reinelt, G., and Schnörr, C., ?Symmetry-free SDP Relaxations for Affine Subspace Clustering?. 2016.

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.

J.  Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., ?Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts?, in Proc. SSVM, 2015.

E. - M.  Didden, Thorarinsdottir, T. L., Lenkoski, A., and Schnörr, C., ?Shape from Texture using Locally Scaled Point Processes?, Image Anal. Stereol., vol. 34, pp. 161-170, 2015.

J. H.  Kappes, Petra, S., Schnörr, C., and Zisler, M., ?TomoGC: Binary Tomography by Constrained Graph Cuts?, in Proc. GCPR, 2015.

F.  Rathke, Schmidt, S., and Schnörr, C., ?Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization?, Medical Image Analysis, vol. 18, pp. 781-794, 2014.

F.  Lenzen, Lellmann, J., Becker, F., and Schnörr, C., ?Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets?, SIAM J. Imag. Sci., vol. 7, pp. 2139?2174, 2014.

S.  Petra, Schnörr, C., and Schröder, A., ?Critical Parameter Values and Reconstruction Properties of Discrete Tomography: Application to Experimental Fluid Dynamics?. 2012.

J.  Lellmann and Schnörr, C., ?Continuous Multiclass Labeling Approaches and Algorithms?, CoRR, vol. abs/1102.5448, 2011.

A.  Nicola, Petra, S., Popa, C., and Schnörr, C., ?A general extending and constraining procedure for linear iterative methods?, Int. J. Comp. Math., 2011.

J.  Lellmann and Schnörr, C., ?Regularizers for Vector-Valued Data and Labeling Problems in Image Processing?, Control Systems and Computers, vol. 2, pp. 43?54, 2011.

B.  Savchynskyy, Kappes, J. H., Schmidt, S., and Schnörr, C., ?A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling?, in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2011.

J.  Lellmann and Schnörr, C., ?Continuous Multiclass Labeling Approaches and Algorithms?, Univ. of Heidelberg, 2010.

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.

J. H.  Kappes, Schmidt, S., and Schnörr, C., ?MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation?, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, pp. 735?747.

A.  Vlasenko and Schnörr, C., ?Physically Consistent and Efficient Variational Denoising of Image Fluid Flow Estimates?, IEEE Trans. Image Proc., vol. 19, pp. 586-595, 2010.

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.

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.

F.  Lauer and Schnörr, C., ?Spectral Clustering of Linear Subspaces for Motion Segmentation?, in Proc. IEEE Int. Conf. Computer Vision (ICCV'09), Kyoto, Japan, 2009.

S.  Petra and Schnörr, C., ?TomoPIV meets Compressed Sensing?, IWR, University of Heidelberg, 2009.

S.  Petra and Schnörr, C., ?TomoPIV meets Compressed Sensing?, Pure Math. Appl., vol. 20, pp. 49 ? 76, 2009.

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.

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.

J. H.  Kappes and Schnörr, C., ?MAP-Inference for Highly-Connected Graphs with DC-Programming?, in Pattern Recognition ? 30th DAGM Symposium, 2008, vol. 5096, pp. 1?10.

S.  Munder, Schnörr, C., and Gavrila, D. M., ?Pedestrian Detection and Tracking Using a Mixture of View-Based Shape-Texture Models?, IEEE Trans. Intell. Transp. Systems, vol. 9, pp. 333-343, 2008.

A.  Vlasenko and Schnörr, C., ?Physically Consistent Variational Denoising of Image Fluid Flow Estimates?, in Pattern Recognition ? 30th DAGM Symposium, 2008, vol. 5096, pp. 406?415.

S.  Petra, Schröder, A., Wieneke, B., and Schnörr, C., ?On Sparsity Maximization in Tomographic Particle Image Reconstruction?, in Pattern Recognition ? 30th DAGM Symposium, 2008, vol. 5096, pp. 294?303.

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.

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.

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.

J.  Giebel, Gavrila, D. M., and Schnörr, C., ?A Bayesian Framework for Multi-cue 3D Object Tracking?, in Computer Vision ? ECCV 2004, 2004, vol. 3024, pp. 241-252.

S.  Weber, Schüle, T., Hornegger, J., and Schnörr, C., ?Binary Tomography by Iterating Linear Programs from Noisy Projections?, in Combinatorial Image Analysis, Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'04), 2004, vol. 3322, pp. 38?51.

J.  Yuan, Schnörr, C., Kohlberger, T., and Ruhnau, P., ?Convex Set-Based Estimation of Image Flows?, in ICPR 2004 ? 17th Int. Conf. on Pattern Recognition, Cambridge, UK, 2004, vol. 1, pp. 124-127.

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.

J.  Keuchel, Schnörr, C., Schellewald, C., and Cremers, D., ?Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming?, vol. 25, pp. 1364?1379, 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.

T.  Kohlberger, Schnörr, C., Bruhn, A., and Weickert, J., ?Domain Decomposition for Parallel Variational Optical Flow Computation?, in Pattern Recognition, Proc. 25th DAGM Symposium, 2003, vol. 2781, pp. 196?203.

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.

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.

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.

S.  Weber, Schüle, T., Schnörr, C., and Hornegger, J., ?A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections?, in Bildverarbeitung für die Medizin 2003, 2003, pp. 41?45.

S.  Weber, Schnörr, C., and Hornegger, J., ?A Linear Programming Relaxation for Binary Tomography with Smoothness Priors?, in Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'03), Palermo, Italy, 2003.

M.  Heiler and Schnörr, C., ?Natural Statistics for Natural Image Segmentation?, in Proc. IEEE Int. Conf. Computer Vision (ICCV 2003), Nice, France, 2003, pp. 1259-1266.

C.  Schellewald and Schnörr, C., ?Subgraph Matching with Semidefinite Programming?, in Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'03), Palermo, Italy, 2003.

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.  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.  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.

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.

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.

J.  Keuchel, Schellewald, C., Cremers, D., and Schnörr, C., ?Convex Relaxations for Binary Image Partitioning and Perceptual Grouping?, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 353?360.

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.

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.

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.  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.

C.  Schellewald, Keuchel, J., and Schnörr, C., ?Image labeling and grouping by minimizing linear functionals over cones?, in Proc. Third Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'01), INRIA, Sophia Antipolis, France, 2001, vol. 2134, pp. 267?282.

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.

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.

M.  Wulf, Stiehl, H. S., and Schnörr, C., ?On the computational rôle of the primate retina?, in Proc. 2nd ICSC Symposium on Neural Computation (NC 2000), Berlin, Germany, 2000.

C.  Schnörr, Ed., ?Künstliche Intelligenz: Special Issue on Medical Computer Vision?, vol. 3. 2000.

J.  Weickert and Schnörr, C., ?PDE?Based Preprocessing of Medical Images?, Künstliche Intelligenz, vol. 3, pp. 5?10, 2000.

C.  Schnörr, ?Variational Adaptive Smoothing and Segmentation?, in Computer Vision and Applications: A Guide for Students and Practitioners, San Diego, 2000, pp. 459?482.

C.  Schnörr and Weickert, J., ?Variational Image Motion Computation: Theoretical Framework, Problems and Perspectives?, in Mustererkennung 2000, Kiel, Germany, 2000.

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.

M.  Wulf, Stiehl, H. S., and Schnörr, C., ?A model of spatiotemporal receptive fields in the primate retina?, in Proc. 1st Göttingen Conf. German Neurosci. Soc., 1999, vol. II.

M.  Wulf, Stiehl, H. S., and Schnörr, C., ?Modeling spatiotemporal receptive fields in the primate retina?, in Proc. Cognitive Neurosci. Conf., Bremen, Germany, 1999.

W.  Peckar, Schnörr, C., Rohr, K., and Stiehl, H. ?S., ?Parameter-Free Elastic Deformation Approach for 2D and 3D Registration Using Prescribed Displacements?, J. Math. Imaging and Vision, vol. 10, pp. 143?162, 1999.

J.  Weickert and Schnörr, C., ?Räumlich?zeitliche Berechnung des optischen Flusses mit nichtlinearen flussabhängigen Glattheitstermen?, in Mustererkennung 1999, 1999, pp. 317?324.

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.

J.  Heers, Schnörr, C., and Stiehl, H. S., ?A class of parallel algorithms for nonlinear variational image segmentation?, in Proc. Noblesse Workshop on Non?Linear Model Based Image Analysis (NMBIA'98), Glasgow, Scotland, 1998.

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.

J.  Heers, Schnörr, C., and Stiehl, H. ?S., ?Investigation of Parallel and Globally Convergent Iterative Schemes for Nonlinear Variational Image Smoothing and Segmentation?, in Proc. IEEE Int. Conf. Image Proc., Chicago, 1998.

W.  Peckar, Schnörr, C., Rohr, K., Stiehl, H. ?S., and Spetzger, U., ?Linear and Incremental Estimation of Elastic Deformations in Medical Registration Using Prescribed Displacements?, Machine Graphics & Vision, vol. 7, pp. 807?829, 1998.

W.  Peckar, Schnörr, C., Rohr, K., and Stiehl, H. S., ?Non-Rigid Image Registration Using a Parameter-Free Elastic Model?, in 9th British Machine Vision Conference (BMVC`98), Southampton/UK, 1998, pp. 134?143.

J.  Heers, Schnörr, C., and Stiehl, H. ?S., ?Parallele und global konvergente iterative Minimierung nichtlinearer Variationsansätze zur adaptiven Glättung und Segmentation von Bildern?, in Mustererkennung 1998, Heidelberg, 1998.

K.  Wiehler, Grigat, R. ?R., Heers, J., Schnörr, C., and Stiehl, H. ?S., ?Real?Time Adaptive Smoothing with a 1D Nonlinear Relaxation Network in Analogue VLSI Technology?, in Mustererkennung 1998, Heidelberg, 1998.

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.

C.  Schnörr, ?Variational approaches to Image Segmentation and Feature Extraction?. University of Hamburg, Comp. Sci. Dept., Hamburg, Germany, 1998.

S.  Gerloff, Hagemann, A., Schnörr, C., Tieck, S., Stiehl, H. S., Dombrowski, R., Dreyer, M., and Wiesendanger, R., ?Semi?Automated Analysis of SXM Images?, in Proc. 9th Int. Conf. on Scanning Tunneling Microscopy/Spectroscopy and Related Techniques (STM'97), Hamburg, Germany, 1997.

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.

C.  Schnörr, ?Convex Variational Segmentation of Multi-Channel Images?, in Proc. 12th Int. Conf. on Analysis and Optimization of Systems: Images, Wavelets and PDE's, Paris, 1996, vol. 219.

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.

C.  Schnörr, ?Repräsentation von Bilddaten mit einem konvexen Variationsansatz?, in Mustererkennung 1996, Berlin, Heidelberg, 1996, pp. 21?28.

C.  Schnörr, ?Representation of Images by a Convex Variational Diffusion Approach?, FB Informatik, Universität Hamburg, FBI-HH-M-256/96, 1996.

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.

C.  Schnörr and Peckar, W., ?Motion-Based Identification of Deformable Templates?, in Proc. 6th Int. Conf. on Computer Analysis of Images and Patterns (CAIP '95), Prague, Czech Republic, 1995, vol. 970, pp. 122-129.

C.  Schnörr, ?Bewegungssegmentation von Bildfolgen durch die Minimierung konvexer nicht-quadratischer Funktionale?, in Mustererkennung 1994, 1994, vol. 5, pp. 178?185.

J.  Heikkonen, Koikkalainen, P., and Schnörr, C., ?Building Trajectories via Selforganization from Spatiotemporal Features?, in 12th Int. Conf. on Pattern Recognition, Jerusalem, Israel, 1994.

R.  Sprengel, Schnörr, C., and Neumann, B., ?Detection of Visual Data Transitions in Nonlinear Parameter Space?, in Mustererkennung 1994, 1994, vol. 5, pp. 315?323.

C.  Schnörr and Sprengel, R., ?A Nonlinear Regularization Approach to Early Vision?, Biol. Cybernetics, vol. 72, pp. 141?149, 1994.

C.  Schnörr, ?Segmentation of Visual Motion by Minimizing Convex Non-Quadratic Functionals?, in 12th Int. Conf. on Pattern Recognition, Jerusalem, Israel, 1994.

C.  Schnörr, ?Unique Reconstruction of Piecewise Smooth Images by Minimizing Strictly Convex Non-Quadratic Functionals?, vol. 4, pp. 189?198, 1994.

C.  Schnörr, Niemann, H., and Kopecz, J., ?Architekturkonzepte zur Bildauswertung?, in Grundlagen und Anwendungen der Künstlichen Intelligenz, 17. Fachtagung für Künstliche Intelligenz, Berlin, 1993, pp. 268?274.

C.  Schnörr, ?On Functionals with Greyvalue-Controlled Smoothness Terms for Determining Optical Flow?, pami, vol. 15, pp. 1074?1079, 1993.

R.  Sprengel and Schnörr, C., ?Nichtlineare Diffusion zur Integration visueller Daten - Anwendung auf Kernspintomogramme?, in Mustererkennung 1993, 15. DAGM-Symposium, 1993, pp. 134?141.

C.  Schnörr, ?Computation of Discontinuous Optical Flow by Domain Decomposition and Shape Optimization?, ijcv, vol. 8, pp. 153?165, 1992.

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.

C.  Schnörr, ?Determining Optical Flow for Irregular Domains by Minimizing Quadratic Functionals of a Certain Class?, ijcv, vol. 6, pp. 25?38, 1991.

C.  Schnörr, ?Funktionalanalytische Methoden zur Bestimmung von Bewegungsinformation aus TV-Bildfolgen?. Fakultät für Informatik, Universität Karlsruhe (TH), 1991.

D.  Bister, Rohr, K., and Schnörr, C., ?Automatische Bestimmung der Trajektorien von sich bewegenden Objekten aus einer Grauwertbildfolge?, in Mustererkennung 1990, 12. DAGM-Symposium, Oberkochen-Aalen, 1990, vol. 254, pp. 44?51.

C.  Schnörr, ?Computation of Discontinuous Optical Flow by Domain Decomposition and Shape Optimization?, in Proc. British Machine Vision Conference, Oxford/UK, 1990, pp. 109?114.

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.