Determining generative models of objects under varying illumination: Shape and albedo from multiple images using SVD and integrability

International Journal of Computer Vision

Volumn 35, Issue 3, 1999, Pages 203-222

  Yuille, A L ^a   Snow, D ^a   Epstein, R ^a   Belhumeur, P N ^b  

^a SMITH KETTLEWELL EYE RESEARCH INSTITUTE   (United States)

^b NONE

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; EIGENVALUES AND EIGENFUNCTIONS; IMAGE ANALYSIS; IMAGE QUALITY; IMAGE RECONSTRUCTION; ITERATIVE METHODS; STATISTICAL METHODS;

SINGULAR VALUE DECOMPOSITION (SVD);

OBJECT RECOGNITION;

EID: 0033281747     PISSN: 09205691     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1008180726317     Document Type: Article

References (42)

1. Atick, J.J., Griffin, P.A., and Redlich, A.N. 1996. Statistical approach to shape from shading: Reconstruction of 3-dimensional face surfaces from single 2-dimensional images. Neural Computation, 8(6):1321-1340.
2. Belhumeur, P.N. and Kriegman, D.J. 1996. What is the set of images of an object under all possible lighting conditions. In Proc. IEEE Conf. on Comp. Vision and Patt. Recog., pp. 270-277.
3. Belhumeur, P., Kriegman, D., and Yuille, A.L. 1997. The Bas-relief ambiguity. In Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition, CVPR97, Puerto Rico.
4. Biederman, I. 1987. Recognition-by-components: A theory of human image understanding. Psychological Review, 94:115-147.
5. Epstein, R. 1996. Learning object representation from greyscale images. Ph.D. Thesis. Division of Applied Sciences. Harvard University.
6. Epstein, R., Hallinan, P.W., and Yuille, A.L. 1995. ± Eigenimages suffice: An empirical investigation of low-dimensional lighting models. In Proceedings of IEEE Workshop on Physics-Based Modeling in Computer Vision, pp. 108-116.
7. Epstein, R., Yuille, A.L., and Belhumeur, P.N. 1996. Learning object representations from lighting variations. In Object Representation in Computer Vision II, J. Ponce, A. Zisserman, and M. Hebert (Eds.), Springer Lecture Notes in Computer Science 1144.
8. Fan, J. and Wolff, L.B. 1997. Surface curvature and shape reconstruction from unknown multiple illumination and integrability. Computer Vision and Image Understanding, 65(2):347-359.
9. Forsyth, D. and Zisserman, A. 1991. Reflections on Shading. IEEE Trans. Pattern Analysis and Machine Intelligence, 13(7):671-679.
10. Freeman, W. and Tennenbaum, 1997. Learning bilinear models for two-factor problems in vision. In Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition, CVPR97, Puerto Rico.
11. Georghiades, A., Kriegman, D., and Belhumeur, P. 1998. Illumination cones for recognition under variable lighting. In Proceedings Computer Vision and Pattern Recognition, CVPR'98.
12. Grenander, U., Chou, Y., and Keenan, K.M. 1991. HANDS: A Pattern Theoretic Study of Biological Shapes. Springer-Verlag: New York.
13. Hallinan, P.W. 1994. A low-dimensional lighting representation of human faces for arbitrary lighting conditions. In Proc. IEEE Conf. on Comp. Vision and Patt. Recog., pp. 995-999.
14. Hallinan, P.W. 1995. A deformable model for face recognition under arbitrary lighting conditions. Ph.D. Thesis. Division of Applied Sciences, Harvard University.
15. Hayakawa, K. 1994. Photometric Stereo under a light source with arbitrary motion. Journal of the Optical Society of America A, 11(11).
16. Horn, B.K.P. and Brooks, M.J. (Eds.). 1989. Shape from Shading. MIT Press: Cambridge MA.
17. Huber, P.J. 1980. Robust Statistics. John Wiley and Sons: New York.
18. Iwahori, Y., Woodham, R.J., and Bagheri, A. 1995. Principal components analysis and neural network implementation of photometric stereo. In Proceedings of the IEEE Workshop on Physics-Based Modeling in Computer Vision, pp. 117-125.
19. Kriegman, D. and Belhumeur, P.N. 1998. What shadows reveal about object structure. In Proceedings of the European Conference on Computer Vision, ECCV'98, pp. 399-414.
20. Marr, D. 1982. Vision. Freeman: New York.
21. Moses, Y., Adini, Y., and Ullman, S. 1994. Face recognition: The problem of compensating for changes in illumination direction. In European Conf. on Computer Vision, pp. 286-296.
22. Murase, H. and Nayar, S. 1995. Visual learning and recognition of 3-D objects from appearance. Int. Journal of Computer Vision, 14:5-24.
23. Nayar, S.K., Ikeuchi, K., and Kanade, T. 1991. Surface reflection: Physical and geometrical perspectives." IEEE Trans. Pattern Analysis and Machine Intelligence, 13:611-634.
24. Pentland, A., Moghaddam, B., and Starner. 1994. View-based and modular eigenspaces for face recognition. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 84-91.
25. Poggio, T. and Edelman, S. 1990. A network that learns to recognize three-dimensional objects. Nature, 343:263-266.
26. Poggio, T. and Sung, K. 1994. Example-based learning for view-based human face detection. In Proc. Image Understanding Workshop, pp. II:843-850.
27. Rosch, E., Mervis, C.B., Gray, W.D., Johnson, D.M., and Boyes-Braem, P. 1976. Basic objects in natural categories. Cogn. Psychol., 8:382-439.
28. Shashua, A. 1992. Geometry and photometry in 3D visual recognition. Ph.D. Thesis. MIT.
29. Shum, H-Y., Hebert, M.H. Ikeuchi, K., and Reddy, R. 1997. An integral approach to free-form object modeling. IEEE Transactions of Pattern Analysis and Machine Intelligence, 19(12).
30. Silver, W. 1980. Determining shape and reflectance using multiple images. Ph.D. Thesis. MIT, Cambridge, MA.
31. Teo, P.C., Simoncelli, E.P., and Heeger, D.J. 1997. Efficient linear re-rendering for interactive lighting design. Report No. STAN-CS-TN-97-60. Dept. Computer Science, Stanford University.
32. Sirovitch, L. and Kirby, M. 1987. Low-dimensional procedure for the characterization of human faces. J. Optical Soc. of America A, 2:519-524.
33. Solomon, F. and Ikeuchi, K. 1995. An illumination planner for convex and concave objects. In Proceedings of the IEEE Workshop on Physics-Based Modeling in Computer Vision, June 18, 19, pp. 100-107.
34. Tarr, M.J. 1995. Rotating objects to recognize them: A case study of the role of viewpoint dependency in the recognition of three-dimensional objects. Psychonomic Bulletin and Review, 2(1):55-82.
35. Tarr, M.J. and Bülthoff, H.H. 1995. Is human object recognition better described by geonstructural-descriptions or by multiple-views?" Journal of Experimental Psychology: Human Perception and Performance, 21:6.
36. Tarr, M.J., Bülthoff, H.H., Zabinski, M., and Blanz, V. 1997. To what extent do unique parts influence recognition across changes in viewpoint? Psychological Science, 8(4):282-289.
37. Tarr, M.J., Kersten, D., and Bülthoff, H.H. 1997. Why the visual system might encode the effects of illumination. Submitted to Vision Research.
38. Tarr, M.J. and Pinker, S. 1989. Mental rotation and orientation-dependence in shape recognition. Cognitive Psychology, 21(28):233-282.
39. Taubin, G., Cukierman, F., Sullivan, S., Ponce, J., and Kriegman, D. 1994. Parameterized families of polynomials for bounded algebraic curve and surface fitting. IEEE Trans. Pattern Anal. Mach. Intelligence, 16(3):287-303.
40. Turk, M. and Pentland, A. 1991. Eigenfaces for recognition. J. of Cognitive Neuroscience, 3(1).
41. Woodham, R. 1981. Analyzing images of curved surfaces. Artificial Intelligence, 17:117-140.
42. Yuille, A.L. and Snow, D. 1997. Shape and albedo from multiple images using integrability. In Proceedings of Computer Vision and Pattern Recognition (CVPR'97), Puerto-Rico.