Publications

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Keuchel, J, Schnörr, C, Schellewald, C and Cremers, D (2003). Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming. PAMI. 25 1364--1379
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Keuchel, J, Schellewald, C, Cremers, D and Schnörr, C (2001). Convex Relaxations for Binary Image Partitioning and Perceptual Grouping. Mustererkennung 2001. Springer. 2191 353--360
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Cremers, D, Tischhäuser, F, Weickert, J and Schnörr, C (2002). Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford--Shah functional. Int.~J.~Computer Vision. 50 295--313
Cremers, D, Schnörr, C, Weickert, J and Schellewald, C (2000). Diffusion Snakes Using Statistical Shape Knowledge. Proc.~Algebraic Frames for the Perception-Action Cycle. Springer. 1888 164--174
Cremers, D, Schnörr, C and Weickert, J (2001). Diffusion--Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework. IEEE First Workshop on Variational and Level Set Methods in Computer Vision. IEEE Comp.~Soc. 237--244
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Heiler, M, Cremers, D and Schnörr, C (2001). Efficient Feature Subset Selection For Support Vector Machines. Dept.~Math.~and Comp.~Science
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Breitenreicher, D and Schnörr, C (2009). Intrinsic Second-Order Geometric Optimization for Robust Point Set Registration Without Correspondence. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2009). Springer. 5681 274-287. http://www.springerlink.com/content/1470n7577713069q/PDF icon Technical Report (752.29 KB)
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Cremers, D, Schnörr, C, Weickert, J and Schellewald, C (2000). Learning Translation Invariant Shape Knowledge for Steering Diffusion-Snakes. 3rd Workshop on Dynamic Perception. Akad.~Verlagsges. 9 117--122
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Cremers, D and Schnörr, C (2002). Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization. Pattern Recognition, Proc.~24th DAGM Symposium. Springer. 2449 472--480
Cremers, D, Sochen, N and Schnörr, C (2004). Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. Computer Vision -- ECCV 2004. Springer. 3024 74-86
Cremers, D, Sochen, N and Schnörr, C (2006). Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. IJCV. 66 67-81
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Goldlücke, B, Strekalovskiy, E and Cremers, D (2012). The Natural Vectorial Total Variation which Arises from Geometric Measure Theory. SIAM Journal on Imaging Sciences. 5 537-563
Goldlücke, B, Strekalovskiy, E and Cremers, D (2012). The natural vectorial total variation which arises from geometric measure theory. SIAM Journal on Imaging Sciences
Cremers, D, Kohlberger, T and Schnörr, C (2002). Nonlinear Shape Statistics in Mumford-Shah Based Segmentation. Computer Vision -- ECCV 2002). Springer Verlag. 2351 93--108PDF icon Technical Report (636.58 KB)
Cremers, D, Kohlberger, T and Schnörr, C (2001). Nonlinear Shape Statistics via Kernel Spaces. Mustererkennung 2001. Springer. 2191 269--276PDF icon Technical Report (324.55 KB)
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Cremers, D, Kohlberger, T and Schnörr, C (2003). Shape Statistics in Kernel Space for Variational Image Segmentation. Pattern Recognition. 36 1929--1943PDF icon Technical Report (1.67 MB)
Cremers, D and Schnörr, C (2003). Statistical Shape Knowledge in Variational Motion Segmentation. Image and Vision Comp. 21 77-86
Goldlücke, B, Aubry, M, Kolev, K and Cremers, D (2014). A super-resolution framework for high-accuracy multiview reconstruction. Int. J. Comp. Vision. 106 172--191
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Goldlücke, B, Strekalovskiy, E and Cremers, D (2013). Tight convex relaxations for vector-valued labeling. SIAM Journal on Imaging Sciences
Cremers, D, Sochen, N and Schnörr, C (2003). Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling. Scale Space Methods in Computer Vision. Springer. 2695 388--400PDF icon Technical Report (451.82 KB)
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Keuchel, J, Schnörr, C, Schellewald, C and Cremers, D (2002). Unsupervised Image Partitioning with Semidefinite Programming. Pattern Recognition, Proc.~24th DAGM Symposium. Springer. 2449 141--149
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Bergtholdt, M, Cremers, D and Schnörr, C (2005). Variational Segmentation with Shape Priors. Handbook of Mathematical Models in Computer Vision. Springer. 147-160