Publications

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Peckar, W, Schnörr, C, Rohr, K and Stiehl, H S (1999). Parameter-Free Elastic Deformation Approach for 2D and 3D Registration Using Prescribed Displacements. J.~Math.~Imaging and Vision. 10 143--162
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2014). Partial Optimality by Pruning for MAP-inference with General Graphical Models. IEEE Conference on Computer Vision and Pattern Recognition 2014PDF icon Technical Report (703.34 KB)
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2014). Partial Optimality by Pruning for MAP-inference with General GraphicalModels. CVPR. Proceedings. 1170-1177
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Partial Optimality via Iterative Pruning for the Potts Model. Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM. 477-488
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Partial Optimality via Iterative Pruning for the Potts Model. Scale Space and Variational Methods (SSVM 2013)PDF icon Technical Report (159.71 KB)
(2007). Pattern Recognition -- 29th DAGM Symposium. Springer. 4713
(2007). Pattern Recognition, 29Th Dagm Symposium, Heidelberg, Germany, September 12-14, 2007, Proceedings. Springer
Schnörr, C and Jähne, B (2007). Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14. Springer. 4713
Weickert, J and Schnörr, C (2000). PDE--Based Preprocessing of Medical Images. Künstliche Intelligenz. 3 5--10
Munder, S, Schnörr, C and Gavrila, D M (2008). Pedestrian Detection and Tracking Using a Mixture of View-Based Shape-Texture Models. IEEE Trans.~Intell.~Transp.~Systems. 9 333-343
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
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Persistency by Pruning for General Graphical Models. submitted to NIPS 2013
Denitiu, A, Petra, S, Schnörr, C and Schnörr, C (2014). Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections. Fundamenta Informaticae. 135 73--102PDF icon Technical Report (2.24 MB)
Vlasenko, A and Schnörr, C (2010). Physically Consistent and Efficient Variational Denoising of Image Fluid Flow Estimates. IEEE Trans.~Image Proc. 19 586-595PDF icon Technical Report (2.65 MB)
Vlasenko, A and Schnörr, C (2008). Physically Consistent Variational Denoising of Image Fluid Flow Estimates. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 406--415PDF icon Technical Report (1.6 MB)
Bodnariuc, E, Schiffner, M, Petra, S and Schnörr, C (2016). Plane Wave Acoustic Superposition for Fast Ultrasound Imaging. International Ultrasonics Symposium
Weber, S, Schüle, T and Schnörr, C (2005). Prior Learning and Convex-Concave Regularization of Binary Tomography. Electr.~Notes in Discr.~Math. 20 313-327
Kappes, J H, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2015). Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts. Proc.~SSVM. SpringerPDF icon Technical Report (1.1 MB)
Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Med. Image Anal. 18 781–794
Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Medical Image Analysis. 18 781-794PDF icon Technical Report (4.07 MB)
Schellewald, C and Schnörr, C (2005). Probabilistic Subgraph Matching Based on Convex Relaxation. Proc.~Int.~Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05). Springer. 3757 171-186
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Weickert, J and Schnörr, C (1999). Räumlich--zeitliche Berechnung des optischen Flusses mit nichtlinearen flussabhängigen Glattheitstermen. Mustererkennung 1999. Springer. 317--324
Wiehler, K, Grigat, R -- R, Heers, J, Schnörr, C and Stiehl, H S (1998). Real--Time Adaptive Smoothing with a 1D Nonlinear Relaxation Network in Analogue VLSI Technology. Mustererkennung 1998. Springer
Bruhn, A, Weickert, J, Feddern, C, Kohlberger, T and Schnörr, C (2003). Real-Time Optic Flow Computation with Variational Methods. Proc.~Computer Analysis of Images and Patterns (CAIP'03). Springer. 2756 222-229
Lellmann, J and Schnörr, C (2011). Regularizers for Vector-Valued Data and Labeling Problems in Image Processing. Control Systems and Computers. 2 43--54
Schnörr, (1996). Repräsentation von Bilddaten mit einem konvexen Variationsansatz. Mustererkennung 1996. Springer-Verlag. 21--28
Schnörr, (1996). Representation Of Images By A Convex Variational Diffusion Approach. FB Informatik
Heiler, M and Schnörr, C (2005). Reverse-Convex Programming for Sparse Image Codes. Proc.~Int.~Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05). Springer. 3757 600-616
Breitenreicher, D and Schnörr, C (2010). Robust 3D object registration without explicit correspondence using geometric integration. Machine Vision and Applications. 21 601-611. http://www.springerlink.com/content/g20710062l014241/PDF icon Technical Report (1.65 MB)
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Berger, J, Neufeld, A, Becker, F, Lenzen, F and Schnörr, C (2015). Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations. Scale Space and Variational Methods in Computer Vision (SSVM 2015). Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-18461-6_32PDF icon Technical Report (364.01 KB)
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, (1994). Segmentation of Visual Motion by Minimizing Convex Non-Quadratic Functionals. 12th Int. Conf. on Pattern Recognition
Andres, B, Garbe, C S, Schnörr, C and Jähne, B (2007). Selection of local optical flow models by means of residual analysis. Proceedings of the 29th DAGM Symposium on Pattern Recognition. Springer. 72--81
Andres, B, Hamprecht, F A and Garbe, C S (2007). Selection of Local Optical Flow Models by Means of Residual Analysis. Pattern Recognition. Springer. 4713 72-81PDF icon Technical Report (229.64 KB)
Gerloff, S, Hagemann, A, Schnörr, C, Tieck, S, Stiehl, H S, Dombrowski, R, Dreyer, M and Wiesendanger, R (1997). Semi--Automated Analysis of SXM Images. Proc.~9th Int.~Conf.~on Scanning Tunneling Microscopy/Spectroscopy and Related Techniques (STM'97)

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