Publications

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Journal Article
Lenzen, F, Becker, F, Lellmann, J, Petra, S and Schnörr, C (2013). A class of quasi-variational inequalities for adaptive image denoising and decomposition. Computational Optimization and Applications. Springer Netherlands. 54 371-398. http://dx.doi.org/10.1007/s10589-012-9456-0PDF icon Technical Report (748.66 KB)
Keuchel, J, Schnörr, C, Schellewald, C and Cremers, D (2003). Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming. PAMI. 25 1364--1379
Petra, S and Schnörr, C (2014). Average Case Recovery Analysis of Tomographic Compressive Sensing. Linear Algebra and its Applications. 441 168-198PDF icon Technical Report (1.85 MB)
Gianniotis, N, Schnörr, C, Molkenthin, C and Bora, S S (2015). Approximate variational inference based on a finite sample of Gaussian latent variables. Patt.~Anal.~ApplPDF icon Technical Report (1.4 MB)
Hinterberger, W, Scherzer, O, Schnörr, C and Weickert, J (2002). Analysis of Optical Flow Models in the Framework of Calculus of Variations. Numer. Funct. Anal. Optimiz. 23 69--89
Schüle, T, Weber, S and Schnörr, C (2005). Adaptive Reconstruction of Discrete-Valued Objects from few Projections. Electr.~Notes in Discr.~Math. 20 365-384
Wiehler, K, Heers, J, Schnörr, C, Stiehl, H S and Grigat, R -- R (2001). A 1D analog VLSI implementation for non-linear real-time signal preprocessing. Real--Time Imaging. 7 127--142
In Collection
Bergtholdt, M, Cremers, D and Schnörr, C (2005). Variational Segmentation with Shape Priors. Handbook of Mathematical Models in Computer Vision. Springer. 147-160
Schnörr, C, Schüle, T and Weber, S (2007). Variational Reconstruction with DC-Programming. Advances in Discrete Tomography and Its Applications. Birkhäuser
Vlasenko, A and Schnörr, C (2009). Variational Approaches for Model-Based PIV and Visual Fluid Analysis. Imaging Measurement Methods for Flow Analysis. Springer. 106 247-256PDF icon Technical Report (3.39 MB)
Ruhnau, P, Kohlberger, T, Nobach, H and Schnörr, C (2004). Variational Optical Flow Estimation for Particle Image Velocimetry. Proc. Lasermethoden in der {S}trömungsmeßtechnik. Deutsche {G}esellschaft für {L}aser-{A}nemometrie {GALA} e.V., Karlsruhe
Bergtholdt, M and Schnörr, C (2005). Shape Priors and Online Appearance Learning for Variational Segmentation and Object Recognition in Static Scenes. Pattern Recognition, Proc.~27th DAGM Symposium. Springer. 3663 342--350
Heiler, M, Keuchel, J and Schnörr, C (2005). Semidefinite Clustering for Image Segmentation with A-priori Knowledge. Pattern Recognition, Proc.~27th DAGM Symposium. Springer. 3663 309--317
Rathke, F, Schmidt, S and Schnörr, C (2011). Order preserving and shape prior constrained intra-retinal layer segmentation in optical coherence tomography. Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011). Springer. 6893 370–377
Becker, F, Petra, S and Schnörr, C (2014). Optical Flow. Handbook of Mathematical Methods in Imaging. Springer
Weber, S, Schnörr, C, Schüle, T and Hornegger, J (2005). Binary Tomography by Iterating Linear Programs. Geometric Properties from Incomplete Data. Springer
Petra, S, Schröder, A and Schnörr, C (2009). 3D Tomography from Few Projections in Experimental Fluid Mechanics. Imaging Measurement Methods for Flow Analysis. Springer. 106 63-72PDF icon Technical Report (411.51 KB)
Conference Paper
Schnörr, (1989). Zur Schätzung von Geschwindigkeitsvektorfeldern in Bildfolgen mit einer richtungsabhängigen Glattheitsforderung. Mustererkennung 1989, 11. DAGM-Symposium. Springer-Verlag. 219 294--301
Schmitzer, B and Schnörr, C (2012). Weakly Convex Coupling Continuous Cuts and Shape Priors. Scale Space and Variational Methods (SSVM 2011). 423-434
Gosch, C, Fundana, K, Heyden, A and Schnörr, C (2008). View Point Tracking of Rigid Object Based on Shape Sub-Manifolds. Computer Vision -- ECCV 2008. Springer. 5302 251--263PDF icon Technical Report (523.17 KB)
Becker, F, Lenzen, F, Kappes, J H and Schnörr, C (2011). Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences. 2011 IEEE International Conference on Computer Vision (ICCV). 1692 -- 1699PDF icon Technical Report (4.9 MB)
Becker, F, Lenzen, F, Kappes, J H and Schnörr, C (2011). Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences. 2011 IEEE International Conference on Computer Vision ICCV. 1692-1699
Schnörr, (1999). Variational Methods for Adaptive Image Smoothing and Segmentation. Handbook on Computer Vision and Applications: Signal Processing and Pattern Recognition. Academic Press. 2 451--484
Swoboda, P and Schnörr, C (2013). Variational Image Segmentation and Cosegmentation with the Wasserstein Distance. Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 8081 321--334PDF icon Technical Report (8.06 MB)
Schnörr, C and Weickert, J (2000). Variational Image Motion Computation: Theoretical Framework, Problems and Perspectives. Mustererkennung 2000. Springer
Lenzen, F, Becker, F, Lellmann, J, Petra, S and Schnörr, C (2011). Variational Image Denoising with Adaptive Constraint Sets. Proceedings of the 3nd International Conference on Scale Space and Variational Methods in Computer Vision 2011, in press. Springer. 6667 206-217
Lenzen, F, Becker, F, Lellmann, J, Petra, S and Schnörr, C (2012). Variational Image Denoising with Adaptive Constraint Sets. LNCS. Springer. 206-217PDF icon Technical Report (649.03 KB)
Kohlberger, T, Mémin, E and Schnörr, C (2003). Variational Dense Motion Estimation Using the Helmholtz Decomposition. Scale Space Methods in Computer Vision. Springer. 2695 432--448
Becker, F, Wieneke, B, Yuan, J and Schnörr, C (2008). Variational Correlation Approach to Flow Measurement with Window Adaption. 14th International Symposium on Applications of Laser Techniques to Fluid Mechanics. 1.1.8

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