Low level vision via switchable Markov random fields

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

Volumn , Issue , 2012, Pages 2432-2439

  Lin, Dahua ^a   Fisher, John ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DE-NOISING; EXPERIMENTAL STUDIES; EXPONENTIAL FAMILY; GRAPHICAL STRUCTURES; HEAVY-TAILS; LOW-LEVEL VISION; MARKOV RANDOM FIELDS; SWITCHABLE; VARIABLE STRUCTURES;

MOTION ESTIMATION;

COMPUTER VISION;

EID: 84866670612     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6247957     Document Type: Conference Paper

References (27)

1. M. Black and A. Rangarajan. The outlier process: Unifying line processes and robust statistics. In Proc. of CVPR, 1994. 2
2. Y. Boykov, O. Veksler, and R. Zabih. Fast Approximate Energy Minimization via Graph Cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11):1222-1233, 2001. 1, 7
3. a. Buades, B. Coll, and J.-M. Morel. A Non-local Algorithm for Image Denoising. In proc. of CVPR'05, 2005. 1, 2
4. a. Buades, B. Coll, and J. M. Morel. A Review of Image Denoising Algorithms, with a New One. Multiscale Modeling & Simulation, 4(2):490, 2005. 1, 2
5. G. Gilboa, J. Darbon, S. Osher, and T. Chan. Nonlocal Convex Functionals for Image Regularization, 2006. 1
6. G. Gilboa and S. Osher. Nonlocal Linear Image Regularization and Supervised Segmentation. Multiscale Modeling and Simulation, 6(2):595, 2007. 1, 2, 6
7. S. Kindermann, S. Osher, and P. W. Jones. Deblurring and Denoising of Images by Nonlocal Functionals. Multiscale Modeling & Simulation, 4(4):1091, 2005. 1
8. P. Kohli and M. Kumar. Energy Minimization for Linear Envelope MRFs. In Proc. of CVPR'10, 2010. 1
9. Y. Li and D. Huttenlocher. Learning for Optical Flow Using Stochastic Optimization. In Proc. of ECCV'08, 2008. 1, 2
10. P. Perona and J. Malik. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629-639, 1990. 1, 2
11. J. Polzehl and V. G. Spokoiny. Adaptive weights smoothing with applications to image restoration. J. R. Statist. Soc. B, 62:335-354, 2000. 1
12. J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli. Image Denoising using Scale Mixtures of Gaussians in the Wavelet Domain. IEEE Transactions on Image Processing, 12(11):1338-51, 2003. 1, 2
13. S. Roth and M. J. Black. Fields of Experts: A Framework for Learning Image Priors. In Proc. of CVPR'05, 2005. 1, 2, 6
14. S. Roth and M. J. Black. On the Spatial Statistics of Optical Flow. In Proc. of ICCV'05, 2005. 1
15. S. Roth and M. J. Black. Steerable Random Fields. In Proc. of ICCV'07, 2007. 1, 2
16. K. Samuel and M. Tappen. Learning Optimized MAP Estimates in Continuously-valued MRF Models. In Proc. of CVPR'09, 2009. 1
17. H. Scharr, M. J. Black, and H. W. Haussecker. Image statistics and anisotropic diffusion. In Proc. of ICCV'03, 2003. 2
18. U. Schmidt, Q. Gao, and S. Roth. A Generative Perspective on MRFs in Low-Level Vision. In Proc. of CVPR'10, 2010. 1, 2
19. D. Sun, S. Roth, and M. J. Black. Secrets of Optical Flow Estimation and Their Principles. In Proc. of CVPR'10, 2010. 5
20. M. Tanaka and M. Okutomi. Locally Adaptive Learning for Translation-Variant MRF Image Priors. In Proc. of CVPR'08, 2008. 1
21. M. F. Tappen. Utilizing Variational Optimization to Learn Markov Random Fields. In Proc. of ICCV'07, 2007. 1, 2
22. M. F. Tappen, C. Liu, E. H. Adelson, and W. T. Freeman. Learning Gaussian Conditional Random Fields for Low- Level Vision. In Proc. of CVPR'07, 2007. 1, 2
23. C. Tomasi and R. Manduchi. Bilateral filtering for gray and color images. In Proc. of ICCV'98, 1998. 1, 2
24. S. Vicente, V. Kolmogorov, and C. Rother. Joint Optimization of Segmentation and Appearance Models. In Proc. of ICCV'09, 2009. 1
25. M. J.Wainwright, T. Jaakkola, and A.Willsky. A New Class of Upper Bounds on the Log Partition Function. IEEE Transactions on Information Theory, 51(7):2313-2335, 2005. 2, 6
26. Y. Weiss and W. T. Freeman. What Makes a Good Model of Natural Images? In Proc. of CVPR'07, 2007. 1, 2
27. S.-C. Zhu, Y.-N. Wu, and D. Mumford. Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling. International Journal of Computer Vision, 27(2):107-126, 1998. 1