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2008
J. H. Kappes and Schnörr, C., “MAP-Inference for Highly-Connected Graphs with DC-Programming”, in Pattern Recognition – 30th DAGM Symposium, 2008, vol. 5096, pp. 1–10.
S. Munder, Schnörr, C., and Gavrila, D. M., “Pedestrian Detection and Tracking Using a Mixture of View-Based Shape-Texture Models”, IEEE Trans. Intell. Transp. Systems, vol. 9, pp. 333-343, 2008.
A. Vlasenko and Schnörr, C., “Physically Consistent Variational Denoising of Image Fluid Flow Estimates”, in Pattern Recognition – 30th DAGM Symposium, 2008, vol. 5096, pp. 406–415.
S. Petra, Schröder, A., Wieneke, B., and Schnörr, C., “On Sparsity Maximization in Tomographic Particle Image Reconstruction”, in Pattern Recognition – 30th DAGM Symposium, 2008, vol. 5096, pp. 294–303.
2009
A. Nicola, Petra, S., Popa, C., and Schnörr, C., “On a general extending and constraining procedure for linear iterative methods”, IWR, University of Heidelberg, 2009.
F. Lauer and Schnörr, C., “Spectral Clustering of Linear Subspaces for Motion Segmentation”, in Proc. IEEE Int. Conf. Computer Vision (ICCV'09), Kyoto, Japan, 2009.
S. Petra and Schnörr, C., “TomoPIV meets Compressed Sensing”, IWR, University of Heidelberg, 2009.
S. Petra and Schnörr, C., “TomoPIV meets Compressed Sensing”, Pure Math. Appl., vol. 20, pp. 49 – 76, 2009.
A. Vlasenko and Schnörr, C., “Variational Approaches for Model-Based PIV and Visual Fluid Analysis”, Imaging Measurement Methods for Flow Analysis, vol. 106. Springer, pp. 247-256, 2009.
2010
J. Lellmann and Schnörr, C., “Continuous Multiclass Labeling Approaches and Algorithms”, Univ. of Heidelberg, 2010.
J. Lellmann, Breitenreicher, D., and Schnörr, C., “Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision”, in European Conference on Computer Vision (ECCV), 2010, vol. 6312, pp. 494–505.
J. H. Kappes, Schmidt, S., and Schnörr, C., “MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation”, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, pp. 735–747.
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.
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.
2011
J. Lellmann and Schnörr, C., “Continuous Multiclass Labeling Approaches and Algorithms”, CoRR, vol. abs/1102.5448, 2011.
A. Nicola, Petra, S., Popa, C., and Schnörr, C., “A general extending and constraining procedure for linear iterative methods”, Int. J. Comp. Math., 2011.
J. Lellmann and Schnörr, C., “Regularizers for Vector-Valued Data and Labeling Problems in Image Processing”, Control Systems and Computers, vol. 2, pp. 43–54, 2011.
B. Savchynskyy, Kappes, J. H., Schmidt, S., and Schnörr, C., “A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling”, in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
2012
S. Petra, Schnörr, C., and Schröder, A., “Critical Parameter Values and Reconstruction Properties of Discrete Tomography: Application to Experimental Fluid Dynamics”. 2012.
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.
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, pp. 2139–2174, 2014.
2015
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”, J. Math. Imag. Vision, vol. 52, pp. 436–458, 2015.
J. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc. SSVM, 2015.
E. - M. Didden, Thorarinsdottir, T. L., Lenkoski, A., and Schnörr, C., “Shape from Texture using Locally Scaled Point Processes”, Image Anal. Stereol., vol. 34, pp. 161-170, 2015.
J. H. Kappes, Petra, S., Schnörr, C., and Zisler, M., “TomoGC: Binary Tomography by Constrained Graph Cuts”, in Proc. GCPR, 2015.
2016
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “The Assignment Manifold: A Smooth Model for Image Labeling”, in Proc. 2nd Int. Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML'16; oral presentation; Grenander best paper award), 2016.
F. Aström and Schnörr, C., “Double-Opponent Vectorial Total Variation”, in Proc. ECCV, 2016.
M. Desana and Schnörr, C., “Expectation Maximization for Sum-Product Networks as Exponential Family Mixture Models”. 2016.
F. Aström and Schnörr, C., “A Geometric Approach to Color Image Regularization”. 2016.
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “A Geometric Approach to Image Labeling”, in Proc. ECCV, 2016.
J. Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Higher-order Segmentation via Multicuts”, Comp. Vision Image Understanding, vol. 143, pp. 104–119, 2016.
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “Image Labeling by Assignment”. 2016.
Y. Censor, Gibali, A., Lenzen, F., and Schnörr, C., “The Implicit Convex Feasibility Problem and Its Application to Adaptive Image Denoising”, J. Comp. Math., vol. 34, pp. 608-623, 2016.
J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, J. Math. Imag. Vision, vol. 56, pp. 221–237, 2016.
E. Bodnariuc, Petra, S., Poelma, C., and Schnörr, C., “Parametric Dictionary-Based Velocimetry for Echo PIV”, in Proc. CGPR, 2016.

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