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H

J. Keuchel, Heiler, M., and Schnörr, C., “Hierarchical Image Segmentation based on Semidefinite Programming”, in Pattern Recognition, Proc. 26th DAGM Symposium, 2004, vol. 3175, pp. 120-128.

J. Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Higher-order Segmentation via Multicuts”, Comp. Vision Image Understanding, vol. 143, pp. 104–119, 2016.

I

C. Schellewald, Keuchel, J., and Schnörr, C., “Image labeling and grouping by minimizing linear functionals over cones”, in Proc. Third Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'01), INRIA, Sophia Antipolis, France, 2001, vol. 2134, pp. 267–282.

R. Hühnerbein, Savarino, F., Aström, F., and Schnörr, C., “Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment”, SIAM J. Imaging Science, vol. 11, pp. 1317–1362, 2018.

R. Hühnerbein, Savarino, F., Aström, F., and Schnörr, C., “Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment”. 2017.

F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “Image Labeling by Assignment”, J. Math. Imag. Vision, vol. 58, pp. 211–238, 2017.

F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “Image Labeling by Assignment”. 2016.

M. Zisler, Aström, F., Petra, S., and Schnörr, C., “Image Reconstruction by Multilabel Propagation”, in Proc. SSVM, 2017, vol. 10302.

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. Heers, Schnörr, C., and Stiehl, H. S., “Investigating a class of iterative schemes and their parallel implementation for nonlinear variational image smoothing and segmentation”, Comp. Sci. Dept., AB KOGS, University of Hamburg, Germany, 283/99, 1999.

J. Heers, Schnörr, C., and Stiehl, H. –S., “Investigation of Parallel and Globally Convergent Iterative Schemes for Nonlinear Variational Image Smoothing and Segmentation”, in Proc. IEEE Int. Conf. Image Proc., Chicago, 1998.

K

C. Schnörr, Ed., “Künstliche Intelligenz: Special Issue on Medical Computer Vision”, vol. 3. 2000.

L

R. Hühnerbein, Savarino, F., Petra, S., and Schnörr, C., “Learning Adaptive Regularization for Image Labeling Using Geometric Assignment”, preprint: arXiv, 2019.

R. Hühnerbein, Savarino, F., Petra, S., and Schnörr, C., “Learning Adaptive Regularization for Image Labeling Using Geometric Assignment”, in Proc. SSVM, 2019.

M. Heiler and Schnörr, C., “Learning Sparse Image Codes by Convex Programming”, in Proc. Tenth IEEE Int. Conf. Computer Vision (ICCV'05), Beijing, China, 2005, pp. 1667-1674.

M. Heiler and Schnörr, C., “Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming”, J. Mach. Learning Res., vol. 7, pp. 1385–1407, 2006.

W. Peckar, Schnörr, C., Rohr, K., Stiehl, H. –S., and Spetzger, U., “Linear and Incremental Estimation of Elastic Deformations in Medical Registration Using Prescribed Displacements”, Machine Graphics & Vision, vol. 7, pp. 807–829, 1998.

S. Weber, Schüle, T., Schnörr, C., and Hornegger, J., “A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections”, Methods of Information in Medicine, vol. 43, pp. 320–326, 2004.

S. Weber, Schüle, T., Schnörr, C., and Hornegger, J., “A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections”, in Bildverarbeitung für die Medizin 2003, 2003, pp. 41–45.

S. Weber, Schnörr, C., and Hornegger, J., “A Linear Programming Relaxation for Binary Tomography with Smoothness Priors”, in Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'03), Palermo, Italy, 2003.

E. Bodnariuc, Petra, S., Schnörr, C., and Voorneveld, J., “A Local Spatio-Temporal Approach to Plane Wave Ultrasound Particle Image Velocimetry”, in Proc. GCPR, 2017.

F. Rathke, Desana, M., and Schnörr, C., “Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans”, in Proc. MICCAI, 2017.

M

F. Aström, Hühnerbein, R., Savarino, F., Recknagel, J., and Schnörr, C., “MAP Image Labeling Using Wasserstein Messages and Geometric Assignment”, in Proc. SSVM, 2017, vol. 10302.

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.

M. Welk, Weickert, J., Becker, F., Schnörr, C., Feddern, C., and Burgeth, B., “Median and related local filters for tensor-valued images”, Signal Processing, vol. 87, pp. 291-308, 2007.

M. Wulf, Stiehl, H. S., and Schnörr, C., “A model of spatiotemporal receptive fields in the primate retina”, in Proc. 1st Göttingen Conf. German Neurosci. Soc., 1999, vol. II.

M. Wulf, Stiehl, H. S., and Schnörr, C., “Modeling spatiotemporal receptive fields in the primate retina”, in Proc. Cognitive Neurosci. Conf., Bremen, Germany, 1999.

C. Schnörr and Peckar, W., “Motion-Based Identification of Deformable Templates”, in Proc. 6th Int. Conf. on Computer Analysis of Images and Patterns (CAIP '95), Prague, Czech Republic, 1995, vol. 970, pp. 122-129.

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.

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.

N

M. Heiler and Schnörr, C., “Natural Image Statistics for Natural Image Segmentation”, Int. J. Comp. Vision, vol. 63, pp. 5–19, 2005.

M. Heiler and Schnörr, C., “Natural Statistics for Natural Image Segmentation”, in Proc. IEEE Int. Conf. Computer Vision (ICCV 2003), Nice, France, 2003, pp. 1259-1266.

R. Sprengel and Schnörr, C., “Nichtlineare Diffusion zur Integration visueller Daten - Anwendung auf Kernspintomogramme”, in Mustererkennung 1993, 15. DAGM-Symposium, 1993, pp. 134–141.

C. Schnörr and Sprengel, R., “A Nonlinear Regularization Approach to Early Vision”, Biol. Cybernetics, vol. 72, pp. 141–149, 1994.

W. Peckar, Schnörr, C., Rohr, K., and Stiehl, H. S., “Non-Rigid Image Registration Using a Parameter-Free Elastic Model”, in 9th British Machine Vision Conference (BMVC`98), Southampton/UK, 1998, pp. 134–143.

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