The illumination-invariant recognition of 3D objects using local color invariants

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 18, Issue 2, 1996, Pages 206-210

  Slater, David ^a   Healey, Glenn ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Color; Color vision.color constancy; Illumination correction; Illumination invariant; Machine vision; Object recognition

Indexed keywords

ALGORITHMS; COLOR IMAGE PROCESSING; COMPUTATIONAL GEOMETRY; COMPUTER VISION; DATA ACQUISITION; DATABASE SYSTEMS; FEATURE EXTRACTION; IMAGING SYSTEMS; INVARIANCE; LIGHTING; MODELS; THREE DIMENSIONAL;

COLOR INVARIANTS; HYPOTHESIS VERIFICATION; ILLUMINATION CORRECTION; ILLUMINATION INVARIANT; IMAGE GEOMETRY; PIXEL DISTRIBUTION; POSE ESTIMATION; SPECTRAL REFLECTANCE;

OBJECT RECOGNITION;

EID: 0030083601     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.481544     Document Type: Article

References (17)

1. A.K. Jain and P.J. Flynn, eds., Three-Dimensional Object Recognition Systems. Amsterdam: Elsevier Science, 1993.
2. P. Besl and R. Jain, "Three-dimensional object recognition," ACM Computing Surveys, vol. 17, no. 1, pp. 75-145, Mar. 1985.
3. T.O. Binford, "Survey of model-based image analysis systems," The Int'l J. Robotics Research, vol. 1, no. 1, pp. 18-64, Spring 1982.
4. R. Chin and C. Dyer, "Model-based recognition in robot vision," ACM Computing Surveys, vol. 18, no. 1, pp. 67-108, Mar. 1986.
5. D.P. Huttenlocher and S. Ullman, "Recognizing solid objects by alignment," M'l J. Computer Vision, vol. 5, no. 2, pp. 195-212,1990.
6. D. Lowe, Perceptual organization and visual recognition, Norwell, Mass: Kluwer Academic Publishers, 1985.
7. W.E.L. Crimson and D.P. Huttenlocher, "On the sensitivity of the hough transform for object recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 3, pp. 255-274,1990.
8. S. Dickinson, A. Pentland, and A. Rosenfeld, "3-d shape recovery vising distributed aspect matching," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 174-198,1992.
9. H. Murase and S.K. Nayar, "Visual learning and recognition of 3-d objects from appearance," Int'l ]. Computer Vision, vol. 14, no. 1, pp. 5-24,1995.
10. S.K. Nayar and R.M. Bolle, "Reflectance based object recognition," Int'l}. Computer Vision, in press.
11. K. Sato, K. Ikeuchi, and T. Kanade, "Model based recognition of specular objects using sensor models," Computer Vision, Graphics, and Image Processing: Image Understanding, vol. 55, no. 2, pp. 155-169,1992.
12. M. Swain and D. Ballard, "Color indexing," Int'l J. Computer Vision, vol. 7, no. 1, pp. 11-32,1991.
13. B. Funt and G. Finlayson, "Color constant color indexing," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 5, pp. 522-529, May 1995.
14. G. Healey and D. Slater, "Global color constancy: Recognition of objects by use of illumination invariant properties of color distributions," J. Opt. Soc. Am. A, vol. 11, no. 11, pp. 3,003-3,010, Nov. 1994.
15. W. Silver, "Determining shape and reflectance using multiple images," Master's thesis, EECS Dept, MIT, June 1980.
16. D. Slater and G. Healey, "Combining color and geometric information for the illumination invariant recognition of 3-D objects," Electrical and Computer Engineering Technical Report ECE-94-12-Ü1, Univ. of Calif., Irvine, 1994.
17. C. Taubin and D. Cooper, "Object recognition based on moment (or algebraic) invariants," Geometric Invariance in Computer Vision, J. Mundy and A. Zisserman, eds., pp. 375-397, Cambridge, Mass: MIT Press, 1992.