Publications

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Author Title [ Type(Desc)] Year
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Journal Article
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, Stahl, A and Schnörr, C (2007). Variational Estimation of Experimental Fluid Flows with Physics-Based Spatio-Temporal Regularization. Measurement Science and Technology. 18 755-763PDF icon Technical Report (842.06 KB)
Heitz, D, Mémin, E and Schnörr, C (2010). Variational fluid flow measurements from image sequences: synopsis and perspectives. Exp.~Fluids. 48 369-393PDF icon Technical Report (1.91 MB)
Bruhn, A, Weickert, J, Feddern, C, Kohlberger, T and Schnörr, C (2005). Variational optic flow computation in real-time. IEEE Trans.~Image Proc. 14 608--615
Weickert, J and Schnörr, C (2001). Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint. J.~Math.~Imaging and Vision. 14 245--255
Ruhnau, P, Kohlberger, T, Nobach, H and Schnörr, C (2005). Variational Optical Flow Estimation for Particle Image Velocimetry. Experiments in Fluids. 38 21--32PDF icon Technical Report (1.21 MB)
Becker, F, Lenzen, F, Kappes, J H and Schnörr, C (2013). Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences. International Journal of Computer Vision. 105 (3) 269-297
Becker, F, Lenzen, F, Kappes, J H and Schnörr, C (2013). Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences. International Journal of Computer Vision. Springer US. 105 269--297. http://dx.doi.org/10.1007/s11263-013-0639-7PDF icon Technical Report (15.4 MB)
Miscellaneous
Schmitzer, B and Schnörr, C (2013). Contour Manifolds and Optimal Transport
Petra, S, Schnörr, C and Schröder, A (2012). Critical Parameter Values and Reconstruction Properties of Discrete Tomography: Application to Experimental Fluid Dynamics. http://arxiv.org/abs/1209.4316
Schmitzer, B and Schnörr, C (2014). Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein ModesPDF icon Technical Report (2.9 MB)
Kappes, J H, Speth, M, Reinelt, G and Schnörr, C (2013). Higher-order Segmentation via Multicuts. ArXiv e-printsPDF icon Technical Report (1.07 MB)
Berger, J, Lenzen, F, Becker, F, Neufeld, A and Schnörr, C (2015). Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. http://arxiv.org/abs/1507.06810PDF icon Technical Report (4.42 MB)
Schnörr, (2001). Statistische Mustererkennung
PhD Thesis
Schnörr, (1991). Funktionalanalytische Methoden zur Bestimmung von Bewegungsinformation aus TV-Bildfolgen. Fakultät für Informatik, Universität Karlsruhe (TH)
Schnörr, (1998). Variational approaches to Image Segmentation and Feature Extraction. University of Hamburg, Comp.~Sci.~Dept.
Techreport
Petra, S, Popa, C and Schnörr, C (2009). Accelerating Constrained Sirt With Applications In Tomographic Particle Image Reconstruction. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/9477PDF icon Technical Report (3.33 MB)
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
Lellmann, J and Schnörr, C (2010). Continuous Multiclass Labeling Approaches And Algorithms. Univ. of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/10460/
Lellmann, J, Kappes, J H, Yuan, J, Becker, F and Schnörr, C (2008). Convex Multi-Class Image Labeling By Simplex-Constrained Total Variation. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/8759/PDF icon Technical Report (2.6 MB)
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
Kohlberger, T, Schnörr, C, Bruhn, A and Weickert, J (2003). Domain Decomposition For Variational Optical Flow Computation. Dept.~Math.~and Comp.~Science
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
Heiler, M, Cremers, D and Schnörr, C (2001). Efficient Feature Subset Selection For Support Vector Machines. Dept.~Math.~and Comp.~Science
Petra, S, Popa, C and Schnörr, C (2008). Extended And Constrained Cimmino-Type Algorithms With Applications In Tomographic Image Reconstruction. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/8798/PDF icon Technical Report (2.13 MB)
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/9761PDF icon Technical Report (799.47 KB)
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
Lellmann, J, Lenzen, F and Schnörr, C (2011). Optimality Bounds For A Variational Relaxation Of The Image Partitioning Problem. IPA group, Heidelberg University. http://arxiv.org/abs/1112.0974
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
Schnörr, (1996). Representation Of Images By A Convex Variational Diffusion Approach. FB Informatik
Petra, S and Schnörr, C (2009). Tomopiv Meets Compressed Sensing. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/9760PDF icon Technical Report (646.75 KB)
Bruhn, A, Weickert, J, Feddern, C, Kohlberger, T and Schnörr, C (2003). Variational Optic Flow Computation In Real-Time. Dept.~Math.~and Comp.~Science

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