Toward guaranteed illumination models for non-convex objects

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 937-944

  Zhang, Yuqian ^a   Mu, Cun ^a   Kuo, Han Wen ^a   Wright, John ^a  

^a Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

Illumination cone model; Lambertian surface; Nonconvex object; Object instance verification

Indexed keywords

FACE RECOGNITION;

DIMENSIONALITY REDUCTION; ILLUMINATION CONE; ILLUMINATION VARIATION; LAMBERTIAN SURFACES; LOW-RANK DECOMPOSITION; NONCONVEX; OBJECT DETECTION AND RECOGNITION; WORST-CASE PERFORMANCE;

ACCEPTANCE TESTS;

EID: 84898771090     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.120     Document Type: Conference Paper

References (23)

1. R. Basri and D. Jacobs. Lambertian reflectance and linear subspaces. PAMI, 25:218-233, 2003.
2. P. Belhumeur and D. Kriegman. What is the set of images of an object under all possible illumination conditions IJCV, 28:270-277, 1998.
3. E. Bronshteyn and D. Ivanov. The approximation of convex sets by polyhedra. Siberian Math. J., 1976.
4. E. Cand̀es, X. Li, Y. Ma, and J. Wright. Robust principal component analysis JACM, 58(7), May 2011.
5. V. Chandrasekaran, S. Sanghavi, P. Parillo, and A. Wilsky. Rank-sparsity incoherence for matrix decomposition. SIAM Journal on Optimization, 21(2):572-596, 2011.
6. T. Chen, W. Yin, X. S. Zhou, D. Domaniciu, , and T. Huang. Illumination normalization for face recognition and uneven background correction using total variation based image models. In CVPR, 2005.
7. T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active appearance models. PAMI, 23:681-685, 2001.
8. D. Frolova, D. Simakov, and R. Basri. Accuracy of spherical harmonic approximations for images of lambertian objects under far and near lighting. In ECCV, 2004.
9. A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman. From few to many: Illumination cone models for face recognition under variable lighting and pose. PAMI, 23(6):643-660, June 2001.
10. B. He and X. Yuan. On the o(1/n) convergence rate of the douglas-rachford alternating direction method. SIAM Journal on Numerical Analysis, 50(2):700-709, 2012.
11. B. K. Horn. Robot Vision. 1st edition, 1986.
12. K.-C. Lee, J. Ho, and D. J. Kriegman. Acquiring linear subspaces for face recognition under variable lighting. PAMI, 27(5):684-698, May 2005.
13. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, Nov. 2004.
14. X. Mei, H. Ling, and D. Jacobs. Sparse representation of cast shadows via l1-regularized least squares. In ICCV, 2009.
15. R. Ramamoorthi. Analytic pca construction for theoretical analysis of lighting variability in images of a lambertian object. PAMI, 24(10):1322-1333, Oct. 2002.
16. R. Ramamoorthi, M. Koudelka, and P. Belhumeur. A fourier theory for cast shadows. PAMI, 27(2), 2005.
17. A. Savran, B. Sankur, and M. Taha Bilge. Comparative evaluation of 3d vs. 2d modality for automatic detection of facial action units. Pattern Recognition, 45(2):767-782, 2012.
18. A. Shashua and T. Riklin-Raviv. The quotient image: Classbased re-rendering and recognition with varying illuminations. PAMI, 23:129-139, 2001.
19. A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma. Toward a practical face recognition system: Robust alignment and illumination by sparse representation. PAMI, 34(2):372-386, 2012.
20. Y. Wang, L. Zhang, Z. Liu, G. Hua, Z. Wen, Z. Zhang, and D. Samaras. Face relighting from a single image under arbitrary unknown lighting conditions. PAMI, 31(11), 2009.
21. J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma. Robust face recognition via sparse representation. PAMI, 31(2), 2009.
22. L. Wu, A. Ganesh, B. Shi, Y. Matsushita, Y. Wang, and Y. Ma. Robust photometric stereo via low-rank matrix completion and recovery. In ACCV, 2010.
23. X. Zhang, M. Burger, X. Bresson, and S. Osher. Bregmanized nonlocal regularization for deconvolution and sparse reconstruction. SJIS, 3(3):253-276, 2010.