Publications

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Filters: Author is Christoph Schnörr and First Letter Of Title is C [Clear All Filters]

Conference Paper

Bruhn, A, Weickert, J and Schnörr, C (2002). Combining the Advantages of Local and Global Optic Flow Methods. Pattern Recognition, Proc. 24th DAGM Symposium. Springer, Zürich, Switzerland. 2449 454–462

Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Sungwoong, K, Kausler, B X, Lellmann, J, Komodakis, N and Rother, C (2013). A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems. CVPR 2013. Proceedings

Technical Report (1.35 MB)

Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Lellmann, J, Komodakis, N and Rother, C (2013). A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem. CVPR

Technical Report (1.35 MB)

Fundana, K, Heyden, A, Gosch, C and Schnörr, C (2008). Continuous Graph Cuts for Prior-Based Object Segmentation. 19th Int.~Conf.~Patt.~Recog.~(ICPR). 1--4

Technical Report (414.89 KB)

Heiler, M and Schnörr, C (2006). Controlling Sparseness in Non-negative Tensor Factorization. Computer Vision -- ECCV 2006. Springer. 3951 56-67

Technical Report (568.86 KB)

Yuan, J, Steidl, G and Schnörr, C (2008). Convex Hodge Decomposition of Image Flows. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 416--425

Technical Report (290.72 KB)

Lellmann, J, Kappes, J H, Yuan, J, Becker, F, Schnörr, C, Mórken, K and Lysaker, M (2009). Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 150-162

Lellmann, J, Kappes, J H, Yuan, J, Becker, F and Schnörr, C (2009). Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 150-162

Technical Report (1.75 MB)

Lellmann, J, Becker, F and Schnörr, C (2009). Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers. IEEE International Conference on Computer Vision (ICCV). 646 -- 653

Technical Report (930.18 KB)

Lellmann, J, Becker, F and Schnörr, C (2009). Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers. Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan. 646-653

Silvestri, F, Reinelt, G and Schnörr, C (2015). A Convex Relaxation Approach to the Affine Subspace Clustering Problem. Proc.~GCPR

Technical Report (878.63 KB)

Journal Article

Lenzen, F, Becker, F, Lellmann, J, Petra, S and Schnörr, C (2013). A Class of Quasi-Variational Inequalities for Adaptive Image Denoising and Decomposition. Computational Optimization and Applications (COAP). 54 (2) 371-398

Lenzen, F, Becker, F, Lellmann, J, Petra, S and Schnörr, C (2013). A class of quasi-variational inequalities for adaptive image denoising and decomposition. Computational Optimization and Applications. Springer Netherlands. 54 371-398. http://dx.doi.org/10.1007/s10589-012-9456-0

Technical Report (748.66 KB)

Breitenreicher, D, Lellmann, J and Schnörr, C (2013). COAL: a generic modelling and prototyping framework for convex optimization problems of variational image analysis. Optimization Methods and Software. 28 1081-1094. http://www.tandfonline.com/doi/abs/10.1080/10556788.2012.672571

Technical Report (1.69 MB)

Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. Int.~J.~Comp.~Vision

Technical Report (5.12 MB)

Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2014). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. CoRR. abs/1404.0533. http://hci.iwr.uni-heidelberg.de/opengm2/

Technical Report (3.32 MB)

Technical Report (1.5 MB)

Rathke, F and Schnörr, C (2015). A Computational Approach to Log-Concave Density Estimation. An. St. Univ. Ovidius Constanta. 23 151-166

Technical Report (1.07 MB)

Rathke, F and Schnörr, C (2015). A Computational Approach to Log-Concave Density Estimation. An. St. Univ. Ovidius Constanta. 23 151-166

Lellmann, J and Schnörr, C (2011). Continuous Multiclass Labeling Approaches and Algorithms. SIAM J.~Imag.~Sci. 4 1049-1096

Technical Report (4.31 MB)

Yuan, J, Schnörr, C and Steidl, G (2009). Convex Hodge Decomposition and Regularization of Image Flows. J.~Math.~Imag.~Vision. 33 169-177

Technical Report (1003.75 KB)

Swoboda, P and Schnörr, C (2013). Convex Variational Image Restoration with Histogram Priors. SIAM J.~Imag.~Sci. 6 1719-1735

Technical Report (553.54 KB)

Petra, S, Schnörr, C and Schröder, A (2013). Critical Parameter Values and Reconstruction Propertiesof Discrete Tomography: Application to Experimental FluidDynamics. Fundamenta Informaticae. 125 285--312

Technical Report (1.42 MB)

Miscellaneous

Schmitzer, B and Schnörr, C (2013). Contour Manifolds and Optimal Transport

Techreport

Lellmann, J, Kappes, J H, Yuan, J, Becker, F and Schnörr, C (2008). Convex Multi-Class Image Labeling By Simplex-Constrained Total Variation. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/8759/

Technical Report (2.6 MB)