Publications

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F
Lellmann, J, Breitenreicher, D and Schnörr, C (2010). Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision. European Conference on Computer Vision (ECCV). Springer Berlin / Heidelberg. 6312 494--505PDF icon Technical Report (1.94 MB)
Garbe, C S, Schnörr, C and Jähne, B (2007). Fluid flow estimation through integration of physical flow configurations. Proceedings of the 29th DAGM Symposium on Pattern Recognition. Springer. 92--101
G
Nicola, A, Petra, S, Popa, C and Schnörr, C (2011). A general extending and constraining procedure for linear iterative methods. Int.~J.~Comp.~Math. http://dx.doi.org/10.1080/00207160.2011.634002PDF icon Technical Report (633.79 KB)
Nicola, A, Petra, S, Popa, C and Schnörr, C (2009). On A General Extending And Constraining Procedure For Linear Iterative Methods. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/9761PDF icon Technical Report (799.47 KB)
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 Hendrik, Swoboda, P and Schnörr, C (2013). Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. NIPS
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
Kappes, J Hendrik, Speth, M, Andres, B, Reinelt, G and Schnörr, C (2011). Globally Optimal Image Partitioning by Multicuts. EMMCVPR. Springer
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)
Schmitzer, B and Schnörr, C (2014). Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein ModesPDF icon Technical Report (2.9 MB)
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/s10851-014-0546-8PDF icon Technical Report (1.97 MB)
Karim, R, Bergtholdt, M, Kappes, J H and Schnörr, C (2007). Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification. Pattern Recognition -- 29th DAGM Symposium. Springer. 4713 395-404PDF icon Technical Report (491.56 KB)
M
Kappes, J H and Schnörr, C (2008). MAP-Inference for Highly-Connected Graphs with DC-Programming. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 1--10PDF icon Technical Report (1.91 MB)
Kappes, J H, Beier, T and Schnörr, C (2014). MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves. Computer Vision - {ECCV} 2014 Workshops - Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part {II}. http://dx.doi.org/10.1007/978-3-319-16181-5_37PDF icon Technical Report (557.49 KB)
Kappes, J Hendrik, Beier, T and Schnörr, C (2014). MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves. International Workshop on Graphical Models in Computer Vision
Welk, M, Becker, F, Schnörr, C and Weickert, J (2005). Matrix-Valued Filters as Convex Programs. Scale-Space 2005. Springer. 3459 204–216
Welk, M, Weickert, J, Becker, F, Schnörr, C, Feddern, C and Burgeth, B (2007). Median and related local filters for tensor-valued images. Signal Processing. 87 291-308PDF icon Technical Report (1007.29 KB)
Breitenreicher, D and Schnörr, C (2011). Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data. Int. J. Comp. Vision. 92 32–52. http://www.springerlink.com/content/v266873267180602/
Breitenreicher, D and Schnörr, C (2011). Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data. Int.~J.~Comp.~Vision. 92 32--52. http://www.springerlink.com/content/v266873267180602/PDF icon Technical Report (4.3 MB)
Schmitzer, B and Schnörr, C (2013). Modelling convex shape priors and matching based on the Gromov-Wasserstein distance. Journal of Mathematical Imaging and Vision. 46 143-159PDF icon Technical Report (957.78 KB)

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