References - Sparse Representations and Compressive Sensing for Imaging and Vision - page 96

64. A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman. From few to many: Ilumination cone
models for face recognition under variable lighting and pose. IEEE Trans. Pattern Analysis
and Machine Intelligence , 23(6):643-660, June 2001.
65. T. Goldstein and S. Osher. The split Bregman algorithm for L1 regularized problems.
Technical report, UCLA CAM.
66. J. W. Goodman. Introduction to Fourier optics . Englewood, CO: Roberts and Company, 2005.
67.N.Gopalsami,T.W.Elmer,S.Liao,R.Ahern,A.Heifetz,A.C.Raptis,M.Luessi,
S. D. Babacan, and A. K. Katsaggelos. Compressive sampling in passive millimeter-wave
imaging. In Proc. SPIE , volume 8022, 2011.
68. I. F. Gorodnitsky and B. D. Rao. Sparse signal reconstruction from limited data using
FOCUSS: A re-weighted minimum norm algorithm. IEEE Transactions on Signal Processing ,
45(3):600616, March 1997.
69. R. Gribonval and M. Nielsen. Sparse representations in unions of bases. IEEE Transactions
on Information Theory , 49(12):3320 - 3325, dec. 2003.
70. W. Guoa and W. Yin. Edgecs: Edge guided compressive sensing reconstruction. Rice
University CAAM Technical Report TR10-02 , 2010.
71. K. Huang and S. Aviyente. Sparse representation for signal classification. NIPS , 19:609-616,
2007.
72. A.K. Jain, S. Prabhakar, L. Hong, and S. Pankanti. Filterbank-based fingerprint matching.
IEEE Transactions on Image Processing , 9(5):846-859, May 2000.
73. Zhuolin Jiang, Zhe Lin, and L.S. Davis. Learning a discriminative dictionary for sparse coding
via label consistent k-svd. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE
Conference on , pages 1697 -1704, june 2011.
74. S-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky. An interior-point method for
large-scale

1 -regularized least squares. IEEE Journal of Selected Topics in Signal Processing ,
1(4):606-617, Dec. 2007.
75. E. Kokiopoulou and P. Frossard. Semantic coding by supervised dimensionality reduction.
IEEE Trans. Multimedia , 10(5):806-818, Aug. 2008.
76. P. Kovesi. Shapelets correlated with surface normals produce surfaces. In ICCV , volume 2,
pages 994 -1001 Vol. 2, oct. 2005.
77. B. Krishnapuram, L. Carin, M.A.T. Figueiredo, and A.J. Hartemink. Sparse multinomial
logistic regression: fast algorithms and generalization bounds. IEEE Transactions on Pattern
Analysis and Machine Intelligence , 27(6):957 -968, June 2005.
78. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching
for recognizing natural scene categories. In Computer Vision and Pattern Recognition, 2006
IEEE Computer Society Conference on , volume 2, pages 2169 - 2178, 2006.
79. M. Lustig, D. Donoho, and M. J. Pauly. Sparse MRI: The Application of Compressed Sensing
for Rapid MR Imaging. Magnetic Resonance in Medicine , 58(6):1182-1195, Dec. 2007.
80. M. Lustig, D.L. Donoho, J.M. Santos, and J.M. Pauly. Compressed sensing mri. IEEE Signal
Processing Magazine , 25(2):72 -82, march 2008.
81. X. Feng M. Yang, L. Zhang and D. Zhang. Fisher discrimination dictionary learning for sparse
representation, 2011. ICCV .
82. J. Mairal, F. Bach, J. Pnce, G. Sapiro, and A. Zisserman. Discriminative learned dictionaries
for local image analysis. Proc. of the Conference on Computer Vision and Pattern Recogni-
tion , 2008.
83. J. Mairal, F. Bach, and J. Ponce. Task-driven dictionary learning. IEEE Transactions on
Pattern Analysis and Machine Intelligence , 34(4):791 -804, april 2012.
84. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Supervised dictionary learning.
Advances in Neural Information Processing Systems , 2008.
85. J.N. Mait, D.A. Wikner, M.S. Mirotznik, J. van der Gracht, G.P. Behrmann, B.L. Good, and
S.A. Mathews. 94-GHz imager with extended depth of field. IEEE Transactions on Antennas
and Propagation , 57(6):1713 -1719, june 2009.
86. A. Maleki and D.L. Donoho. Optimally tuned iterative reconstruction algorithms for
compressed sensing. IEEE Journal of Selected Topics in Signal Processing , 4(2):330-341,
april 2010.