Publications

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Becker, F, Wieneke, B, Petra, S, Schröder, A and Schnörr, C (2012). Variational Adaptive Correlation Method for Flow Estimation. IEEE Transactions on Image Processing. 21 3053 -- 3065PDF icon Technical Report (18.81 MB)
Schnörr, (2000). Variational Adaptive Smoothing and Segmentation. Computer Vision and Applications: A Guide for Students and Practitioners. Academic Press. 459--482
Ruhnau, P, Gütter, C, Putze, T and Schnörr, C (2005). A variational approach for particle tracking velocimetry. Meas.~Science and Techn. 16 1449-1458
Becker, F, Wieneke, B, Yuan, J and Schnörr, C (2008). A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 335--344PDF icon Technical Report (1.82 MB)
Becker, F, Wieneke, B, Yuan, J and Schnörr, C (2008). A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry". Pattern Recognition -- 30th DAGM Symposium. 5096 335-344
Schnörr, C, Sprengel, R and Neumann, B (1996). A Variational Approach to the Design of Early Vision Algorithms. Computing Suppl. 11 149-165
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)
Schnörr, (1998). Variational approaches to Image Segmentation and Feature Extraction. University of Hamburg, Comp.~Sci.~Dept.
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
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. 1.1.3PDF icon Technical Report (3.37 MB)
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
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)
(2005). Variational, Geometric and Level Sets in Computer Vision (VLSM'05). Springer. 3752
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)
Schnörr, C and Weickert, J (2000). Variational Image Motion Computation: Theoretical Framework, Problems and Perspectives. Mustererkennung 2000. Springer
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, (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
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
Bruhn, A, Weickert, J, Feddern, C, Kohlberger, T and Schnörr, C (2003). Variational Optic Flow Computation In Real-Time. Dept.~Math.~and Comp.~Science
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)
Schnörr, C, Schüle, T and Weber, S (2007). Variational Reconstruction with DC-Programming. Advances in Discrete Tomography and Its Applications. Birkhäuser
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
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 (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)
Bergtholdt, M, Cremers, D and Schnörr, C (2005). Variational Segmentation with Shape Priors. Handbook of Mathematical Models in Computer Vision. Springer. 147-160
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)
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Schmitzer, B and Schnörr, C (2012). Weakly Convex Coupling Continuous Cuts and Shape Priors. Scale Space and Variational Methods (SSVM 2011). 423-434

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