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Publications

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Author Title [ Type(Desc)] Year
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Journal Article
J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, Journal of Mathematical Imaging and Vision, vol. 47, pp. 239-257, 2012.
J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for Variational Relaxations of Optimal Partition Problems”, 2010.
P. Swoboda, Shekhovtsov, A., Kappes, J. Hendrik, Schnörr, C., and Savchynskyy, B., “Partial Optimality by Pruning for MAP-Inference with General Graphical Models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, pp. 1370–1382, 2016.
A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections”, Fundamenta Informaticae, vol. 135, p. 73--102, 2014.PDF icon Technical Report (2.24 MB)
A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections”, Fundamenta Informaticae, vol. 135, pp. 73–102, 2014.
A. Vlasenko and Schnörr, C., “Physically Consistent and Efficient Variational Denoising of Image Fluid Flow Estimates”, IEEE Trans.~Image Proc., vol. 19, pp. 586-595, 2010.PDF icon Technical Report (2.65 MB)
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Med. Image Anal., vol. 18, pp. 781–794, 2014.
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Medical Image Analysis, vol. 18, pp. 781-794, 2014.PDF icon Technical Report (4.07 MB)
D. Breitenreicher and Schnörr, C., “Robust 3D object registration without explicit correspondence using geometric integration”, Machine Vision and Applications, vol. 21, pp. 601-611, 2010.PDF icon Technical Report (1.65 MB)
D. Breitenreicher and Schnörr, C., “Robust 3D object registration without explicit correspondence using geometric integration”, Machine Vision and Applications, vol. 21, pp. 601-611, 2010.
D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, p. 1929--1943, 2003.PDF icon Technical Report (1.67 MB)
D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, pp. 1929–1943, 2003.
C. Schnörr, “Signal and Image Approximation with Level-Set Constraints”, Computing, vol. 81, pp. 137-160, 2007.PDF icon Technical Report (506.8 KB)
J. Yuan, Schnörr, C., and Steidl, G., “Simultaneous Optical Flow Estimation and Decomposition”, SIAM J.~Scientific Computing, vol. 29, pp. 2283-2304, 2007.PDF icon Technical Report (1.16 MB)
F. Lenzen, Lellmann, J., Becker, F., and Schnörr, C., “Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets”, SIAM J.~Imag.~Sci., vol. 7, p. 2139--2174, 2014.PDF icon Technical Report (802.13 KB)
F. Lenzen, Lellmann, J., Becker, F., and Schnörr, C., “Solving QVIs for Image Restoration with Adaptive Constraint Sets”, SIAM Journal on Imaging Sciences (SIIMS), in press, 2014.
D. Breitenreicher, Lellmann, J., and Schnörr, C., “Sparse Template-Based Variational Image Segmentation”, Advances in Adaptive Data Analysis, vol. 3, pp. 149-166, 2011.PDF icon Technical Report (866.28 KB)
D. Breitenreicher, Lellmann, J., and Schnörr, C., “Sparse Template-Based Variational Image Segmentation”, Advances in Adaptive Data Analysis, vol. 3, pp. 149-166, 2011.
D. Cremers and Schnörr, C., “Statistical Shape Knowledge in Variational Motion Segmentation”, Image and Vision Comp., vol. 21, pp. 77-86, 2003.
B. Savchynskyy, Kappes, J. H., Schmidt, S., and Schnörr, C., “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, pp. 1817 - 1823, 2011.
M. Bergtholdt, Kappes, J. H., Schmidt, S., and Schnörr, C., “A Study of Parts-Based Object Class Detection Using Complete Graphs”, Int.~J.~Comp.~Vision, vol. 87, pp. 93-117, 2010.PDF icon Technical Report (2.18 MB)
M. Bergtholdt, Kappes, J. H., Schmidt, S., and Schnörr, C., “A Study of Parts-Based Object Class Detection Using Complete Graphs”, Int. J. Comp. Vision, vol. 87, pp. 93-117, 2010.
S. Petra and Schnörr, C., “TomoPIV meets Compressed Sensing”, Pure Math.~Appl., vol. 20, p. 49 -- 76, 2009.PDF icon Technical Report (409.1 KB)
F. Becker, Wieneke, B., Petra, S., Schröder, A., and Schnörr, C., “Variational Adaptive Correlation Method for Flow Estimation”, IEEE Transactions on Image Processing, vol. 21, 6, pp. 3053 - 3065, 2011.
F. Becker, Wieneke, B., Petra, S., Schröder, A., and Schnörr, C., “Variational Adaptive Correlation Method for Flow Estimation”, IEEE Transactions on Image Processing, vol. 21, p. 3053 -- 3065, 2012.PDF icon Technical Report (18.81 MB)
F. Becker, Wieneke, B., Petra, S., Schröder, A., and Schnörr, C., “Variational Adaptive Correlation Method for Flow Estimation”, IEEE Transactions on Image Processing, vol. 21, pp. 3053 – 3065, 2012.
P. Ruhnau, Stahl, A., and Schnörr, C., “Variational Estimation of Experimental Fluid Flows with Physics-Based Spatio-Temporal Regularization”, Measurement Science and Technology, vol. 18, pp. 755-763, 2007.PDF icon Technical Report (842.06 KB)
D. Heitz, Mémin, E., and Schnörr, C., “Variational fluid flow measurements from image sequences: synopsis and perspectives”, Exp.~Fluids, vol. 48, pp. 369-393, 2010.PDF icon Technical Report (1.91 MB)
A. Bruhn, Weickert, J., Feddern, C., Kohlberger, T., and Schnörr, C., “Variational optic flow computation in real-time”, IEEE Trans. Image Proc., vol. 14, pp. 608–615, 2005.
P. Ruhnau, Kohlberger, T., Nobach, H., and Schnörr, C., “Variational Optical Flow Estimation for Particle Image Velocimetry”, Experiments in Fluids, vol. 38, p. 21--32, 2005.PDF icon Technical Report (1.21 MB)
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, International Journal of Computer Vision, vol. 105 (3), pp. 269-297, 2013.
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, International Journal of Computer Vision, vol. 105, no. 3, p. 269--297, 2013.PDF icon Technical Report (15.4 MB)
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, International Journal of Computer Vision, vol. 105, pp. 269–297, 2013.
Miscellaneous
B. Schmitzer and Schnörr, C., “Contour Manifolds and Optimal Transport”. 2013.
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”. 2014.PDF icon Technical Report (2.9 MB)

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