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Publications

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Author Title Type [ Year(Desc)]
Filters: Author is Florian Becker  [Clear All Filters]
2005
M. Welk, Becker, F., Schnörr, C., and Weickert, J., “Matrix-Valued Filters as Convex Programs”, in Scale-Space 2005, 2005, vol. 3459, pp. 204–216.
J. Yuan, Schnörr, C., Steidl, G., and Becker, F., “A Study of Non-Smooth Convex Flow Decomposition”, in Proc. Variational, Geometric and Level Set Methods in Computer Vision, 2005, vol. 3752, pp. 1–12.
2007
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. 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.PDF icon Technical Report (1007.29 KB)
2008
J. Lellmann, Kappes, J. H., Yuan, J., Becker, F., and Schnörr, C., “Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation”, IWR, University of Heidelberg, 2008.PDF icon Technical Report (2.6 MB)
F. Becker and Schnörr, C., “Decomposition of Quadratric Variational Problems”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 325--334.
F. Becker and Schnörr, C., “Decomposition of Quadratric Variational Problems”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 325--334.PDF icon Technical Report (1.29 MB)
F. Becker, Wieneke, B., Yuan, J., and Schnörr, C., “A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry"”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, pp. 335-344.
F. Becker, Wieneke, B., Yuan, J., and Schnörr, C., “A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry”, in Pattern Recognition – 30th DAGM Symposium, 2008, vol. 5096, pp. 335–344.
F. Becker, Wieneke, B., Yuan, J., and Schnörr, C., “A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 335--344.PDF icon Technical Report (1.82 MB)
F. Becker, Wieneke, B., Yuan, J., and Schnörr, C., “Variational Correlation Approach to Flow Measurement with Window Adaption”, in 14th International Symposium on Applications of Laser Techniques to Fluid Mechanics, 2008, p. 1.1.8.
F. Becker, Wieneke, B., Yuan, J., and Schnörr, C., “Variational Correlation Approach to Flow Measurement with Window Adaption”, in 14th International Symposium on Applications of Laser Techniques to Fluid Mechanics, 2008, p. 1.1.3.
F. Becker, Wieneke, B., Yuan, J., and Schnörr, C., “Variational Correlation Approach to Flow Measurement with Window Adaption”, in 14th International Symposium on Applications of Laser Techniques to Fluid Mechanics, 2008, p. 1.1.3.PDF icon Technical Report (3.37 MB)
2009
J. Lellmann, Kappes, J. H., Yuan, J., Becker, F., Schnörr, C., Mórken, K., and Lysaker, M., “Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation”, in Scale Space and Variational Methods in Computer Vision (SSVM 2009), 2009, vol. 5567, pp. 150-162.
J. Lellmann, Kappes, J. H., Yuan, J., Becker, F., and Schnörr, C., “Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation”, in Scale Space and Variational Methods in Computer Vision (SSVM 2009), 2009, vol. 5567, pp. 150-162.PDF icon Technical Report (1.75 MB)
J. Lellmann, Becker, F., and Schnörr, C., “Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers”, in Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan, 2009, pp. 646-653.
J. Lellmann, Becker, F., and Schnörr, C., “Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers”, in IEEE International Conference on Computer Vision (ICCV), 2009, p. 646 -- 653.PDF icon Technical Report (930.18 KB)
F. Becker, “Variational Correlation and Decomposition Methods for Particle Image Velocimetry”. Heidelberg University, Faculty of Mathematics and Computer Sciences, Heidelberg, Germany, 2009.
2011
F. Becker, Wieneke, B., Petra, S., Schröder, A., and Schnörr, C., “Variational Adaptive Correlation Method for Flow Estimation”, IEEE Transactions on Image Processing, vol. 21, 6, pp. 3053 - 3065, 2011.
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “Variational Image Denoising with Adaptive Constraint Sets”, in Proceedings of the 3nd International Conference on Scale Space and Variational Methods in Computer Vision 2011, in press, 2011, vol. 6667, pp. 206-217.
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, in 2011 IEEE International Conference on Computer Vision (ICCV), 2011, p. 1692 -- 1699.PDF icon Technical Report (4.9 MB)
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, in 2011 IEEE International Conference on Computer Vision (ICCV), 2011, pp. 1692 – 1699.
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, in 2011 IEEE International Conference on Computer Vision ICCV, 2011, pp. 1692-1699.
2012
F. Becker, Wieneke, B., Petra, S., Schröder, A., and Schnörr, C., “Variational Adaptive Correlation Method for Flow Estimation”, IEEE Transactions on Image Processing, vol. 21, p. 3053 -- 3065, 2012.PDF icon Technical Report (18.81 MB)
F. Becker, Wieneke, B., Petra, S., Schröder, A., and Schnörr, C., “Variational Adaptive Correlation Method for Flow Estimation”, IEEE Transactions on Image Processing, vol. 21, pp. 3053 – 3065, 2012.
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “Variational Image Denoising with Adaptive Constraint Sets”, in Proceedings of the 3rd International Conference on Scale Space and Variational Methods in Computer Vision 2011, 2012, pp. 206-217.
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “Variational Image Denoising with Adaptive Constraint Sets”, in LNCS, 2012, pp. 206-217.PDF icon Technical Report (649.03 KB)
2013
F. Lenzen, Becker, F., and Lellmann, J., “Adaptive Second-Order Total Variation: An Approach Aware of Slope Discontinuities”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM, 2013, vol. 7893, pp. 61-73.
F. Lenzen, Becker, F., and Lellmann, J., “Adaptive Second-Order Total Variation: An Approach Aware of Slope Discontinuities”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2013, 2013, vol. 54, no. 2, p. 371--398.PDF icon Technical Report (702.08 KB)
S. Petra, Schnörr, C., Becker, F., and Lenzen, F., “B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2013, 2013, vol. 7893, pp. 110-124.PDF icon Technical Report (1.15 MB)
S. Petra, Schnörr, C., Becker, F., and Lenzen, F., “B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM, 2013, pp. 110-124.
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “A class of quasi-variational inequalities for adaptive image denoising and decomposition”, Computational Optimization and Applications, vol. 54, pp. 371-398, 2013.PDF icon Technical Report (748.66 KB)
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “A Class of Quasi-Variational Inequalities for Adaptive Image Denoising and Decomposition”, Computational Optimization and Applications (COAP), vol. 54 (2), pp. 371-398, 2013.
F. Lenzen, Kim, K. In, Schäfer, H., Nair, R., Meister, S., Becker, F., and Garbe, C. S., “Denoising Strategies for Time-of-Flight Data”, Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications, vol. 8200. Springer, pp. 25-45, 2013.PDF icon Technical Report (961.62 KB)
F. Lenzen, Kim, K. I., Schäfer, H., Nair, R., Meister, S., Becker, F., and Garbe, C. S., “Denoising Strategies for Time-of-Flight Data”, in Time-of-Flight Imaging: Algorithms, Sensors and Applications, 2013, vol. 8200, pp. 24-25.

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