Publications

Export 277 results:
Author Title [ Type(Desc)] Year
Filters: Author is Christoph Schnörr  [Clear All Filters]
Journal Article
Schmitzer, B and Schnörr, C (2013). Modelling convex shape priors and matching based on the Gromov-Wasserstein distance. Journal of Mathematical Imaging and Vision. 46 143-159PDF icon Technical Report (957.78 KB)
Bruhn, A, Weickert, J, Kohlberger, T and Schnörr, C (2006). A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods. Int.~J.~Computer Vision. 70 257-277PDF icon Technical Report (447.65 KB)
Cremers, D, Sochen, N and Schnörr, C (2006). Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. IJCV. 66 67-81
Heiler, M and Schnörr, C (2005). Natural Image Statistics for Natural Image Segmentation. Int.~J.~Comp.~Vision. 63 5--19
Schnörr, C and Sprengel, R (1994). A Nonlinear Regularization Approach to Early Vision. Biol. Cybernetics. 72 141--149
Ruhnau, P and Schnörr, C (2007). Optical Stokes Flow Estimation: An Imaging-Based Control Approach. Exp.~in Fluids. 42 61--78PDF icon Technical Report (1.54 MB)
Lellmann, J, Lenzen, F and Schnörr, C (2012). Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. Journal of Mathematical Imaging and Vision. Springer. 47 239-257PDF icon Technical Report (616.16 KB)
Lellmann, J, Lenzen, F and Schnörr, C (2013). Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. Journal of Mathematical Imaging and Vision. 47 (3) 239-257
Lellmann, J, Lenzen, F and Schnörr, C (2010). Optimality Bounds for Variational Relaxations of Optimal Partition Problems
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
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
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)
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
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)
Lellmann, J and Schnörr, C (2011). Regularizers for Vector-Valued Data and Labeling Problems in Image Processing. Control Systems and Computers. 2 43--54
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)
Cremers, D, Kohlberger, T and Schnörr, C (2003). Shape Statistics in Kernel Space for Variational Image Segmentation. Pattern Recognition. 36 1929--1943PDF icon Technical Report (1.67 MB)
Schnörr, (2007). Signal and Image Approximation with Level-Set Constraints. Computing. 81 137-160PDF icon Technical Report (506.8 KB)
Yuan, J, Schnörr, C and Steidl, G (2007). Simultaneous Optical Flow Estimation and Decomposition. SIAM J.~Scientific Computing. 29 2283-2304PDF icon Technical Report (1.16 MB)
Lenzen, F, Lellmann, J, Becker, F and Schnörr, C (2014). Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets. SIAM J.~Imag.~Sci. 7 2139--2174PDF icon Technical Report (802.13 KB)
Lenzen, F, Lellmann, J, Becker, F and Schnörr, C (2014). Solving QVIs for Image Restoration with Adaptive Constraint Sets. SIAM Journal on Imaging Sciences (SIIMS), in press
Breitenreicher, D, Lellmann, J and Schnörr, C (2011). Sparse Template-Based Variational Image Segmentation. Advances in Adaptive Data Analysis. 3 149-166PDF icon Technical Report (866.28 KB)
Cremers, D and Schnörr, C (2003). Statistical Shape Knowledge in Variational Motion Segmentation. Image and Vision Comp. 21 77-86
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
Savchynskyy, B, Kappes, J H, Schmidt, S and Schnörr, C (2011). A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), accepted as oral presentation. 1817 - 1823
Bergtholdt, M, Kappes, J H, Schmidt, S and Schnörr, C (2010). A Study of Parts-Based Object Class Detection Using Complete Graphs. Int.~J.~Comp.~Vision. 87 93-117. http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s11263-009-0209-1PDF icon Technical Report (2.18 MB)
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
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.phpPDF icon Technical Report (409.1 KB)
Schnörr, (1994). Unique Reconstruction of Piecewise Smooth Images by Minimizing Strictly Convex Non-Quadratic Functionals. JMIV. 4 189--198
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)
Becker, F, Wieneke, B, Petra, S, Schröder, A and Schnörr, C (2011). Variational Adaptive Correlation Method for Flow Estimation. IEEE Transactions on Image Processing. 21, 6 3053 - 3065
Ruhnau, P, Gütter, C, Putze, T and Schnörr, C (2005). A variational approach for particle tracking velocimetry. Meas.~Science and Techn. 16 1449-1458

Pages