Total variation for cyclic structures: Convex relaxation and efficient minimization

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2011, Pages 1905-1911

  Strekalovskiy, Evgeny ^a   Cremers, Daniel ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

PATTERN RECOGNITION;

CONVEX RELAXATION; CYCLIC STRUCTURES; DATA TERMS; END POINTS; LIFTING FRAMEWORK; MEMORY EFFICIENCY; REGULARIZER; TOTAL VARIATION;

RELAXATION PROCESSES;

EID: 80052875236     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995573     Document Type: Conference Paper

References (17)

1. G. Alberti, G. Bouchitté, and G. D. Maso. The Calibration Method for the Mumford-Shah Functional and Freediscontinuity Problems. Calc. Var. Partial Differential Equations, (16(3)):299-333, 2003. 1906, 1911
2. L. Ambrosio, N. Fusco, and D. Pallara. Functions of Bounded Variation and Free Discontinuity Problems. Oxford University Press, USA, 2000. 1906
3. M. Bertalmio, L. Vese, G. Sapiro, and S. Osher. Simultaneous Structure and Texture Image Inpainting. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 2:707, 2003. 1905
4. M. Boerdgen, B. Berkels, M. Rumpf, and D. Cremers. Convex Relaxation for Grain Segmentation at Atomic Scale. Vision, Modelng, and Visualization, 2010. 1910
5. D. C. Ghiglia and M. D. Pritt. Two-Dimensional Phase Unwrapping: Theory, Algorithms, and Software. John Wiley & Sons, 1 edition, 1998. 1905
6. M. Giaquinta, G. Modica, and J. Souček. Cartesian Currents in the Calculus of Variations II: Variational Integrals. Springer Verlag, 1 edition, 1998. 1906
7. K. Kolev, M. Klodt, T. Brox, and D. Cremers. Continuous Global Optimization in Multiview 3D Reconstruction. International Journal of Computer Vision, 84(1):80-96, August 2009. 1905
8. A. Marquina and S. Osher. Image Super-resolution by TV Regularization and Bregman Iteration. Journal of Scientific Computing, (37(3)):367-382, 2008. 1905
9. T. Pock, A. Chambolle, H. Bischof, and D. Cremers. A Convex Relaxation Approach for Computing Minimal Partitions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, Florida, 2009. 1906
10. T. Pock, D. Cremers, H. Bischof, and A. Chambolle. An Algorithm for Minimizing the Piecewise Smooth Mumford-Shah Functional. In IEEE International Conference on Computer Vision (ICCV), Kyoto, Japan, 2009. 1906, 1908, 1909
11. T. Pock, D. Cremers, H. Bischof, and A. Chambolle. Global Solutions of Variational Models with Convex Regularization. SIAM Journal on Imaging Sciences. To appear, 2010. 1906, 1907
12. T. Pock, T. Schoenemann, G. Graber, H. Bischof, and D. Cremers. A Convex Formulation of Continuous Multi-Label Problems. In European Conference on Computer Vision (ECCV), Marseille, France, October 2008. 1906
13. R. T. Rockafellar. Convex Analysis. Princeton University Press, 1996. 1906
14. L. I. Rudin, S. Osher, and E. Fatemi. Nonlinear Total Variation Based Noise Removal Algorithms. Phys. D, 60(1-4):259-268, 1992. 1905, 1909
15. P. Stefanovic, M. Haataja, and N. Provatas. Phase Field Crystal Study of Deformation and Plasticity in Nanocrystalline Materials. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), 80(4):046107+, 2009. 1910
16. F. Steinbruecker, T. Pock, and D. Cremers. Large Displacement Optical Flow Computation without Warping. In IEEE International Conference on Computer Vision (ICCV), Kyoto, Japan, 2009. 1905
17. C. Vogel and M. Oman. Fast, Robust Total Variation-based Reconstruction of Noisy, Blurred Images. IEEE Transactions on Image Processing, (7(6)):813-824, 1998. 1905