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Filters: Author is Daniel Cremers  [Clear All Filters]
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M. Bergtholdt, Cremers, D., and Schnörr, C., “Variational Segmentation with Shape Priors”, Handbook of Mathematical Models in Computer Vision. Springer, pp. 147-160, 2005.
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J. Keuchel, Schnörr, C., Schellewald, C., and Cremers, D., “Unsupervised Image Partitioning with Semidefinite Programming”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 141–149.
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D. Cremers, Sochen, N., and Schnörr, C., “Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling”, in Scale Space Methods in Computer Vision, 2003, vol. 2695, pp. 388–400.
D. Cremers, Sochen, N., and Schnörr, C., “Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling”, in Scale Space Methods in Computer Vision, 2003, vol. 2695, p. 388--400.PDF icon Technical Report (451.82 KB)
B. Goldlücke, Strekalovskiy, E., and Cremers, D., “Tight convex relaxations for vector-valued labeling”, SIAM Journal on Imaging Sciences, 2013.
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B. Goldlücke, Aubry, M., Kolev, K., and Cremers, D., “A super-resolution framework for high-accuracy multiview reconstruction”, Int. J. Comp. Vision, vol. 106, p. 172--191, 2014.
D. Cremers and Schnörr, C., “Statistical Shape Knowledge in Variational Motion Segmentation”, Image and Vision Comp., vol. 21, pp. 77-86, 2003.
D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, pp. 1929–1943, 2003.
D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, p. 1929--1943, 2003.PDF icon Technical Report (1.67 MB)
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D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, 2001, vol. 2191, p. 269--276.PDF icon Technical Report (324.55 KB)
D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 269–276.
D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision – ECCV 2002), 2002, vol. 2351, pp. 93–108.
D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision -- ECCV 2002), 2002, vol. 2351, p. 93--108.PDF icon Technical Report (636.58 KB)
B. Goldlücke, Strekalovskiy, E., and Cremers, D., “The Natural Vectorial Total Variation which Arises from Geometric Measure Theory”, SIAM Journal on Imaging Sciences, vol. 5, pp. 537-563, 2012.
B. Goldlücke, Strekalovskiy, E., and Cremers, D., “The natural vectorial total variation which arises from geometric measure theory”, SIAM Journal on Imaging Sciences, 2012.
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D. Cremers, Sochen, N., and Schnörr, C., “Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation”, ijcv, vol. 66, pp. 67-81, 2006.
D. Cremers, Sochen, N., and Schnörr, C., “Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation”, in Computer Vision – ECCV 2004, 2004, vol. 3024, pp. 74-86.
D. Cremers and Schnörr, C., “Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 472–480.
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D. Cremers, Schnörr, C., Weickert, J., and Schellewald, C., “Learning Translation Invariant Shape Knowledge for Steering Diffusion-Snakes”, in 3rd Workshop on Dynamic Perception, Berlin, Germany, 2000, vol. 9, pp. 117–122.
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D. Breitenreicher and Schnörr, C., “Intrinsic Second-Order Geometric Optimization for Robust Point Set Registration Without Correspondence”, in Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2009), 2009, vol. 5681, pp. 274-287.
D. Breitenreicher and Schnörr, C., “Intrinsic Second-Order Geometric Optimization for Robust Point Set Registration Without Correspondence”, in Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2009), 2009, vol. 5681, pp. 274-287.PDF icon Technical Report (752.29 KB)
E. Töppe, Oswald, M. R., Cremers, D., and Rother, C., “Image-based 3D modeling via cheeger sets”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6492 LNCS, pp. 53–64.
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M. Heiler, Cremers, D., and Schnörr, C., “Efficient Feature Subset Selection for Support Vector Machines”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 21/2001, 2001.
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D. Cremers, Schnörr, C., and Weickert, J., “Diffusion–Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework”, in IEEE First Workshop on Variational and Level Set Methods in Computer Vision, Vancouver, Canada, 2001, pp. 237–244.
D. Cremers, Schnörr, C., Weickert, J., and Schellewald, C., “Diffusion Snakes Using Statistical Shape Knowledge”, in Proc. Algebraic Frames for the Perception-Action Cycle, Kiel, 2000, vol. 1888, pp. 164–174.
D. Cremers, Tischhäuser, F., Weickert, J., and Schnörr, C., “Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford–Shah functional”, Int. J. Computer Vision, vol. 50, pp. 295–313, 2002.
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J. Keuchel, Schellewald, C., Cremers, D., and Schnörr, C., “Convex Relaxations for Binary Image Partitioning and Perceptual Grouping”, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 353–360.
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J. Keuchel, Schnörr, C., Schellewald, C., and Cremers, D., “Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming”, vol. 25, pp. 1364–1379, 2003.