Publications

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Techreport
Petra, S and Schnörr, C (2009). Tomopiv Meets Compressed Sensing. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/9760
Schnörr, (1996). Representation Of Images By A Convex Variational Diffusion Approach. FB Informatik, Universität Hamburg
Schellewald, C, Roth, S and Schnörr, C (2002). Performance Evaluation Of A Convex Relaxation Approach To The Quadratic Assignment Of Relational Object Views. Dept. Math. and Comp. Science, University of Mannheim, Germany
Heers, J, Schnörr, C and Stiehl, H S (1999). 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
Nicola, A, Petra, S, Popa, C and Schnörr, C (2009). On A General Extending And Constraining Procedure For Linear Iterative Methods. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/9761
Heiler, M, Cremers, D and Schnörr, C (2001). Efficient Feature Subset Selection For Support Vector Machines. Dept. Math. and Comp. Science, University of Mannheim, Germany
Neumann, J, Schnörr, C and Steidl, G (2003). Effectively Finding The Optimal Wavelet For Hybrid Wavelet - Large Margin Signal Classification. Dept. Math. and Comp. Science, University of Mannheim, Germany
Kohlberger, T, Schnörr, C, Bruhn, A and Weickert, J (2003). Domain Decomposition For Variational Optical Flow Computation. Dept. Math. and Comp. Science, University of Mannheim, Germany
Schüle, T, Schnörr, C, Weber, S and Hornegger, J (2003). Discrete Tomography By Convex-Concave Regularization And D.c. Programming. Dept. Math. and Comp. Science, University of Mannheim, Germany
Lellmann, J and Schnörr, C (2010). Continuous Multiclass Labeling Approaches And Algorithms. Univ. of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/10460/
Schellewald, C, Roth, S and Schnörr, C (2001). Application Of Convex Optimization Techniques To The Relational Matching Of Object Views. Dept. Math. and Comp. Science, University of Mannheim, Germany
Journal Article
Ruhnau, P, Kohlberger, T, Nobach, H and Schnörr, C (2005). Variational Optical Flow Estimation for Particle Image Velocimetry. Experiments in Fluids. 38 21–32
Weickert, J and Schnörr, C (2001). Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint. J. Math. Imaging and Vision. 14 245–255
Heitz, D, Mémin, E and Schnörr, C (2010). Variational fluid flow measurements from image sequences: synopsis and perspectives. Exp. Fluids. 48 369-393
Ruhnau, P, Stahl, A and Schnörr, C (2007). Variational Estimation of Experimental Fluid Flows with Physics-Based Spatio-Temporal Regularization. Measurement Science and Technology. 18 755-763
Schnörr, C, Sprengel, R and Neumann, B (1996). A Variational Approach to the Design of Early Vision Algorithms. Computing Suppl. 11 149-165
Ruhnau, P, Gütter, C, Putze, T and Schnörr, C (2005). A variational approach for particle tracking velocimetry. Meas. Science and Techn. 16 1449-1458
Zern, A, Zisler, M, Petra, S and Schnörr, C (2019). Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment. preprint: arXiv. https://arxiv.org/abs/1904.10863
Zern, A, Zisler, M, Petra, S and Schnörr, C (2020). Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment. Journal of Mathematical Imaging and Vision. https://doi.org/10.1007/s10851-019-00935-7
Schnörr, (1994). Unique Reconstruction of Piecewise Smooth Images by Minimizing Strictly Convex Non-Quadratic Functionals. 4 189–198
Petra, S and Schnörr, C (2009). TomoPIV meets Compressed Sensing. Pure Math. Appl. 20 49 – 76. http://www.mat.unisi.it/newsito/puma/public_html/contents.php
Weickert, J and Schnörr, C (2001). A Theoretical Framework for Convex Regularizers in PDE–Based Computation of Image Motion. Int. J. Computer Vision. 45 245–264
Censor, Y, Petra, S and Schnörr, C (2020). Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. J. Appl. Numer. Optimization (in press; arXiv:1911.05498). 2 15-62. http://jano.biemdas.com/archives/1060
Censor, Y, Petra, S and Schnörr, C (2019). Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. preprint: arXiv. https://arxiv.org/abs/1911.05498
Desana, M and Schnörr, C (2020). Sum-Product Graphical Models. Machine Learning. 109 135–173
Desana, M and Schnörr, C (2019). Sum-Product Graphical Models. Machine Learning. https://doi.org/10.1007/s10994-019-05813-2
Schnörr, (1998). A Study of a Convex Variational Diffusion Approach for Image Segmentation and Feature Extraction. J. of Math. Imag. and Vision. 8 271–292

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