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Schmitzer, B and Schnörr, C (2014). Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes
Schmitzer, B and Schnörr, C (2015). Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes. J. Math. Imag. Vision. 52 436–458. http://link.springer.com/article/10.1007/s10851-014-0546-8
Kappes, J H, Speth, M, Andres, B, Reinelt, G and Schnörr, C (2011). Globally Optimal Image Partitioning by Multicuts. EMMCVPR. Springer. 31-44PDF icon Technical Report (7.3 MB)
Kappes, J H, Speth, M, Andres, B, Reinelt, G and Schnörr, C (2011). Globally Optimal Image Partitioning by Multicuts. EMMCVPR. SpringerPDF icon Technical Report (7.47 MB)
Kappes, J Hendrik, Speth, M, Andres, B, Reinelt, G and Schnörr, C (2011). Globally Optimal Image Partitioning by Multicuts. EMMCVPR. Springer
Andres, B, Kröger, T, Briggmann, K L, Denk, W, Norogod, N, Knott, G W, Köthe, U and Hamprecht, F A (2012). Globally Optimal Closed-Surface Segmentation for Connectomics. ECCV 2012. Proceedings, Part 3. 778-791PDF icon Technical Report (2.72 MB)
Wanner, S, Straehle, C N and Goldlücke, B (2013). Globally Consistent Multi-Label Assignment on the Ray Space of 4D Light Fields. CVPR 2013. Proceedings. 1011-1018
Wanner, S, Straehle, C N and Goldlücke, B (2013). Globally consistent multi-label assignment on the ray space of 4D light fields. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Wanner, S and Goldlücke, B (2012). Globally Consistent Depth Labeling of 4D Lightfields. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Wanner, S and Goldlücke, B (2012). Globally Consistent Depth Labeling of 4D Light Fields. CVPR. Proceedings. 41-48
Schnörr, C, Stiehl, H - S and Grigat, R - R (1996). On Globally Asymptotically Stable Continuous-Time CNNs for Adaptive Smoothing of Multidimensional Signals. Proc. 4th IEEE Int. Workshop on Cellular Neural Networks and their Applications. Seville, Spain
He, K, Rhemann, C, Rother, C, Tang, X and Sun, J (2011). A global sampling method for alpha matting. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2049–2056
Woodford, O J (2009). A Global Perspective on MAP Inference for Low-Level Vision Supplementary material to ICCV submission \# 1536. Optimization
Savchynskyy, B, Kappes, J H, Swoboda, P and Schnörr, C (2013). Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. NIPSPDF icon Technical Report (499.17 KB)
Savchynskyy, B, Kappes, J H, Swoboda, P and Schnörr, C (2013). Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. NIPS. Proceedings. 1950-1958
Savchynskyy, B, Kappes, J Hendrik, Swoboda, P and Schnörr, C (2013). Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. NIPS
Michel, F, Kirillov, A, Brachmann, E, Krull, A, Gumhold, S, Savchynskyy, B and Rother, C (2017). Global hypothesis generation for 6D object pose estimation. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 115–124. http://arxiv.org/abs/1612.02287
Dierig, T (2002). Gewinnung von Tiefenkarten aus Fokusserien. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/2461
Savchynskyy, B and Schmidt, S (2013). Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study. Workshop on Inference for Probabilistic Graphical Models at ICCV. Proceedings
Savchynskyy, B and Schmidt, S (2012). Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study. arXiv:1210.4081
Rombach, R, Esser, P and Ommer, B (2021). Geometry-Free View Synthesis: Transformers and no 3D Priors. Proceedings of the Intl. Conf. on Computer Vision (ICCV). https://arxiv.org/abs/2104.07652
Schultz, M (1997). Geometrische Kalibrierung Von Ccd-Kameras. University of Heidelberg
Zeilmann, A, Savarino, F, Petra, S and Schnörr, C (2020). Geometric Numerical Integration of the Assignment Flow. Inverse Problems. 36 034004 (33pp)
Zeilmann, A, Savarino, F, Petra, S and Schnörr, C (2019). Geometric Numerical Integration of the Assignment Flow. Inverse Problems. https://doi.org/10.1088/1361-6420/ab2772
Zeilmann, A, Savarino, F, Petra, S and Schnörr, C (2018). Geometric Numerical Integration of the Assignment Flow. preprint: arXiv. https://arxiv.org/abs/1810.06970
Abu Alhaija, H, Mustikovela, S K, Geiger, A and Rother, C (2018). Geometric Image Synthesis. ACCV. Proceedings, in pressPDF icon Technical Report (1.83 MB)
Abu Alhaija, H, Mustikovela, S Karthik, Geiger, A and Rother, C (2019). Geometric Image Synthesis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11366 LNCS 85–100. https://youtu.be/W2tFCz9xJoU
Zern, A, Rohr, K and Schnörr, C (2018). Geometric Image Labeling with Global Convex Labeling Constraints. EMMCVPR. 10746 533–547
Zern, A, Rohr, K and Schnörr, C (2017). Geometric Image Labeling with Global Convex Labeling Constraints. Proc. EMMCVPR
Aström, F, Petra, S, Schmitzer, B and Schnörr, C (2016). A Geometric Approach to Image Labeling. Proc. ECCV
Aström, F and Schnörr, C (2016). A Geometric Approach to Color Image Regularization. https://arxiv.org/abs/1605.05977
Aström, F and Schnörr, C (2017). A Geometric Approach for Color Image Regularization. Comp. Vision Image Understanding. 165 43–59. https://doi.org/10.1016/j.cviu.2017.10.013
Köthe, U, Andres, B, Kröger, T and Hamprecht, F A (2010). Geometric Analysis of 3D Electron Microscopy Data. Proceedings of Workshop on Discrete Geometry and Mathematical Morphology (WADGMM). 22-26PDF icon Technical Report (1.43 MB)
Gulshan, V, Rother, C, Criminisi, A, Blake, A and Zisserman, A (2010). Geodesic star convexity for interactive image segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 3129–3136
Rubio, J C, Eigenstetter, A and Ommer, B (2015). Generative Regularization with Latent Topics for Discriminative Object Recognition. Pattern Recognition. Elsevier. 48 3871--3880PDF icon Technical Report (5.49 MB)

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