Sparse signal recovery using Markov Random Fields

Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Volumn , Issue , 2009, Pages 257-264

  Cevher, Volkan ^a   Duarte, Marco F ^a   Hegde, Chinmay ^a   Baraniuk, Richard G ^a  

^a RICE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MARKOV PROCESSES; SIGNAL RECONSTRUCTION;

COMPRESSIVE SENSING; LINEAR MEASUREMENTS; MARKOV RANDOM FIELDS; MEASUREMENT PROCESS; MODEL-BASED OPC; NON-ZERO COEFFICIENTS; NYQUIST; RECOVERY ALGORITHMS; SPARSE SIGNAL RECOVERIES; SPARSE SIGNALS;

COMPRESSED SENSING;

EID: 84858788674     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. D. L. Donoho. Compressed sensing. IEEE Trans. Info. Theory, 52(4):1289-1306, Sept. 2006.
2. E. J. Candès. Compressive sampling. In Proc. International Congress of Mathematicians, volume 3, pages 1433-1452, Madrid, Spain, 2006.
3. S. L. Lauritzen. Graphical Models. Oxford University Press, 1996.
4. D. Needell and J. Tropp. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples. Applied and Computational Harmonic Analysis, June 2008. To appear.
5. C. La and M. N. Do. Tree-based orthogonal matching pursuit algorithm for signal reconstruction. In IEEE Int. Conf. Image Processing (ICIP), pages 1277-1280, Atlanta, GA, Oct. 2006.
6. M. F. Duarte, M. B.Wakin, and R. G. Baraniuk. Wavelet-domain compressive signal reconstruction using a hidden Markov tree model. In ICASSP, pages 5137-5140, Las Vegas, NV, April 2008.
7. V. Cevher, A. Sankaranarayanan, M. F. Duarte, D. Reddy, R. G. Baraniuk, and R. Chellappa. Compressive sensing for background subtraction. In ECCV, Marseille, France, Oct. 2008.
8. R. G. Baraniuk, M. Davenport, R. A. DeVore, and M. B.Wakin. A simple proof of the restricted isometry property for random matrices. 2006. To appear in Const. Approx.
9. T. Blumensath and M. E. Davies. Sampling theorems for signals from the union of linear subspaces. 2007. Preprint.
10. B. M. McCoy and T. T. Wu. The two-dimensional Ising model. Harvard Univ. Press, 1973.
11. 1-regularized logistic regression. In Proc. of Advances in NIPS, 2006.
12. D. P. Wipf and B. D. Rao. Sparse bayesian learning for basis selection. IEEE Trans. Sig. Proc., 52(8):2153-2164, August 2004.
13. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, 1988.
14. V. Kolmogorov and R. Zabin. What energy functions can be minimized via graph cuts? IEEE Trans. on Pattern Anal. and Mach. Int., 26(2):147-159, 2004.
15. Y. Boykov, O. Veksler, and R. Zabih. Efficient approximate energy minimization via graph cuts. IEEE Trans. on Pattern Anal. and Mach. Int., 20(12):1222-1239, Nov. 2001. (Pubitemid 33137959)
16. Y. Boykov and V. Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. on Pattern Anal. and Mach. Int., 26(9):1124-1137, Sept. 2004.
17. 1-regularized minimization with applications to compressed sensing. Technical Report TR07-07, Rice University, CAM Dept., 2007.