Reflectance based object recognition

International Journal of Computer Vision

Volumn 17, Issue 3, 1996, Pages 219-240

  Nayar, Shree K ^a   Bolle, Ruud M ^b  

^a Columbia University ^*  (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Indexing; Model acquisition; Object recognition; Object representation; Photometric invariant; Physical properties; Pose estimation; Reflectance ratio; Region ratios; Retinex theory

Indexed keywords

ALGORITHMS; IMAGE PROCESSING; OBJECT RECOGNITION; PATTERN RECOGNITION; VISION;

PHOTOMETRIC INVARIANT; REFLECTANCE RATIO; REGION RATIOS; RETINEX THEORY;

COMPUTER VISION;

EID: 0030110361     PISSN: 09205691     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF00128232     Document Type: Article

References (40)

1. Aho, A.V., Hopcroft, J.E., and Ullman, J.D. 1974. The Design and Analysis of Computer Algorithms. Addison-Wesley: Reading, MA.
2. Ballard, D.H. and Brown, C.M. 1982. Computer Vision. Prentice Hall.
3. Besl, P.J. and Jain, R.C. 1985. Three-dimensional object recognition. ACM Computing Surveys, 17(1):75-145.
4. Blake, A. 1985. Boundary conditions for lightness computation in Mondrian world. Computer Vision, Graphics, and Image Processing, 32:314-327.
5. Born, A. and Wolf, E. 1965. Principles of Optics. Pergamon: London.
6. Burns, J.B., Weiss, R., and Riseman, E. 1990. View variation of point-set and line segment features. In Proc. Image Understanding Workshop, pp. 650-659.
7. Califano, A. and Mohan, R. 1991. Multidimensional indexing for recognizing visual shapes. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 28-34.
8. Chin, R.T. and Dyer, C.R. 1986. Model-based recognition in robot vision. ACM Computing Surveys, 18(1):67-108.
9. Finlayson, G.D. 1992. Colour Object Recognition. M.S. Thesis, Simon Fraser University.
10. Forsyth, D. and Zisserman, A. 1989. Mutual Illumination. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 466-473.
11. Gilchrist, A.L. 1979. The perception of surfaces blacks and whites. Scientific American, pp. 112-124.
12. Gray, S.B. 1971. Local properties of binary images in two dimensions. IEEE Trans, on Computers, 20(5):551-561.
13. Healey, G.E., Shafer, S.A., and Wolff, L.B. (Eds.) 1992. Physics Based Vision: Color, Jones and Bartlett.
14. Horn, B.K.P. 1974. Determining lightness from an image. Computer Graphics and Image Processing, 3(1):277-299.
15. Horn, B.K.P. 1986. Robot Vision. The MIT Press: Cambridge, MA.
16. Hurlbert, A.C. 1989. The computation of color. Ph.D. thesis, Massachusetts Institute of Technology.
17. Huttenlocher, D.P. and Ullman, S. 1990. Recognizing solid objects by alignment with an image. International Journal of Computer Vision, 5(2):195-212.
18. Koenderink, J.J. and van Doom, A.J. 1980. Photometric invariants related to solid shape. Optica Acta, 27(7):981-996.
19. Koenderink, J.J. and van Doom, A.J. 1983. Geometrical modes as a general method to treat diffuse interreflections in radiometry. Journal of Optical Society of America, 73(6):843-850.
20. Lambdan, Y. and Wolfson, H.J. 1988. Geometric hashing: A general and efficient model-based recognition scheme. In Proc. International Conference on Computer Vision, pp. 238-249.
21. Land, E.H. 1964. The retinex. American Scientist, 52(2):247-264.
22. Land, E.H. and McCann, J.J. 1971. Lightness and retinex theory. Journal of Optical Society of America, 61(1): 1-11.
23. Land, E.H. 1983. Recent advances in retinex theory and some implications for cortical computations: Color vision and the natural image. Proc. National Academy of Science, 80:5163-5169.
24. Land, E.H. 1986a. An alternative technique for the computation of the designator in the retinex theory of color vision. Proc. National Academy of Science, 83:3078-3080.
25. Land, E.H. 1986b. Recent advances in retinex theory. Vision Research, 26:7-21.
26. Lumia, R., Shapiro, L., and Zuniga, O, 1983. A new connected components algorithm for virtual memory computers. Computer Vision, Graphics, and Image Processing, 22:287-300.
27. Nagy, G. 1969. Feature Extraction on Binary Patterns. IEEE Trans. on Systems, Science, and Cybernetics, 5(4):273-278.
28. Nayar, S.K. and Bolle, R.M. 1993. Computing reflectance ratios from an image. Pattern Recognition, 26(10):1529-1542.
29. Nayar, S.K., Ikeuchi, K., and Kanade, T. 1990. Determining shape and reflectance of hybrid surfaces by photometric sampling. IEEE Transactions on Robotics and Automation, 6(4): 418-431.
30. Nayar, S.K., Ikeuchi, K., and Kanade, T. 1991a. Shape from Interreflections. International Journal of Computer Vision, 6(3):173-195.
31. Nayar, S.K., Ikeuchi, K., and Kanade, T. 1991b. Surface reflection: Physical and geometrical perspective, IEEE Trans. on Pattern Analysis and Machine Intell., 13(7):611-634.
32. Nayar, S.K., Fang, X., and Boult, I.E. 1993. Removal of specularities using color and polarization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 583-590.
33. Nicol, C.J. 1993. A systolic architecture for labeling the connected components of multi-valued images in real time. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 136-141.
34. Oren, M. and Nayar, S.K. 1993. Generalization of the Lambertian Model. In Proc. Image Understanding Workshop, pp. 1037-1048.
35. Rosenfeld, A. and Pfaltz, J.L, 1966. Sequential operations in digital picture processing. ACM, 13:471-494.
36. Sinha, P. and Adelson, E. 1993. Recovering reflectance and illumination in a world of painted polyhedra. In Proc. International Conference on Computer Vision.
37. Strickler, M.A. 1992. Color and geometry as cues for indexing. Technical Report CS92-22, University of Chicago.
38. Swain, M.J. and Ballard, D.H. 1991. Color Indexing. International Journal of Computer Vision, pp. 11-32.
39. Torrance, K. and Sparrow, E. 1967. Theory for off-specular reflection from rough surfaces. Journal of Optical Society of America, 57:1057-1114.
40. Weinshall, D. 1991. Model-based invariants for 3d vision. Technical Report RC 17705, IBM Thomas J. Watson Research Center.