OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Vol. 12, Iss. 9 — Sep. 1, 1995
  • pp: 1877–1883

Illumination-invariant recognition of texture in color images

Glenn Healey and Lizhi Wang  »View Author Affiliations


JOSA A, Vol. 12, Issue 9, pp. 1877-1883 (1995)
http://dx.doi.org/10.1364/JOSAA.12.001877


View Full Text Article

Enhanced HTML    Acrobat PDF (895 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

We represent texture in a color image by using spatial correlation functions defined within and between sensor bands. This representation has been shown to be useful for surface recognition, but the structure of spatial correlation functions depends on the spectral properties of the scene illumination. Using a linear model for surface spectral reflectance with the same number of parameters as the number of classes of photoreceptors, we show that illumination changes correspond to linear transformations of a surface correlation matrix. From this relationship we derive a distance function for comparing sets of spatial correlation functions that can be used for illumination-invariant recognition. This distance function can be computed efficiently from estimated correlation functions. We demonstrate, using a large body of experiments, that this distance function can be used for accurate surface recognition in the presence of large changes in illumination spectral distribution.

© 1995 Optical Society of America

Citation
Glenn Healey and Lizhi Wang, "Illumination-invariant recognition of texture in color images," J. Opt. Soc. Am. A 12, 1877-1883 (1995)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-12-9-1877


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. G. Healey, S. Shafer, and L. Wolff, eds., Physics-Based Vision: Principles and Practice. COLOR (Jones & Bartlett, Boston, Mass., 1992).
  2. M. Nagao, T. Matsuyama, and Y. Ikeda, "Region extraction and shape analysis in aerial photographs," Comput. Graphics Image Process. 10, 195–223 (1979). [CrossRef]
  3. M. Swain and D. Ballard, "Color indexing," Int. J. Comput. Vision 7, 11–32 (1991). [CrossRef]
  4. 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 11, 3003–3010 (1994). [CrossRef]
  5. R. Chellappa and A. K. Jain, eds., Markov Random Fields: Theory and Applications (Academic, San Diego, Calif., 1993).
  6. F. S. Cohen, Z. Fan, and M. S. Patel, "Classification of rotated and scaled textured images using Gaussian Markov random field models," IEEE Trans. Pattern Anal. Mach. Intell. 13, 192–202 (1991). [CrossRef]
  7. R. Haralick, "Statistical and structural approaches to texture," Proc. IEEE 67, 786–804 (1979). [CrossRef]
  8. R. L. Kashyap and A. Khotanzad, "A model based method for rotation invariant texture classification," IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 472–481 (1986). [CrossRef]
  9. A. Gagalowicz, S. D. Ma, and C. Tournier-Lasserve, "Efficient models for color textures," in Proceedings of the 8th International Conference on Pattern Recognition (IEEE Computer Society Press, Washington, D.C., 1986), pp. 412–414.
  10. D. Panjwani and G. Healey, "Results using random field models for the segmentation of color images of natural scenes," in Proceedings of the Fifth International Conference on Computer Vision (IEEE, Cambridge, Mass., 1995), pp. 714–719.
  11. D. Panjwani and G. Healey, "Selecting neighbors in random field models for color images," in Proceedings of the First IEEE International Conference on Image Processing (Institute of Electrical and Electronics Engineers, New York, 1994). [CrossRef]
  12. R. Kondepudy and G. Healey, "Use of invariants for recognition of three-dimensional color textures," J. Opt. Soc. Am. A 11, 3037–3049 (1994). [CrossRef]
  13. J. Scharcanski, J. K. Hovis, and H. C. Shen, "Color texture representation using multiscale feature boundaries," in Visual Communications and Image Processing, P. Maragos, ed., Proc. Soc. Photo-Opt. Instrum. Eng. 1818, 156–165 (1992).
  14. M. Brill, "A device performing illuminant-invariant assessment of chromatic relations," J. Theor. Biol. 71, 473–478 (1978). [CrossRef] [PubMed]
  15. G. Buchsbaum, "A spatial processor model for object colour perception," J. Franklin Inst. 310, 1–26 (1980). [CrossRef]
  16. P. Sallstrom, "Colour and physics; some remarks concerning the physical aspects of human colour vision," Tech. Rep. 73-09 (Institute of Physics, University of Stockholm, 1973).
  17. L. Maloney and B. Wandell, "Color constancy: a method for recovering surface spectral reflectance," J. Opt. Soc. Am. A 3, 29–33 (1986). [CrossRef] [PubMed]
  18. J. Cohen, "Dependency of the spectral reflectance curves of the Munsell color chips," Psychonom. Sci. 1, 369–370 (1964).
  19. L. Maloney, "Evaluation of linear models of surface spectral reflectance with small numbers of parameters," J. Opt. Soc. Am. A 3, 1673–1683 (1986). [CrossRef] [PubMed]
  20. J. P. S. Parkkinen, J. Hallikainen, and T. Jaaskelainen, "Characteristic spectra of Munsell colors," J. Opt. Soc. Am. A 6, 318–322 (1989). [CrossRef]
  21. D. Forsyth, "A novel algorithm for color constancy," Int. J. Comput. Vision 5, 5–36 (1990). [CrossRef]
  22. J. Ho, B. V. Funt, and M. S. Drew, "Separating a color signal into illumination and surface reflectance components: theory and applications," IEEE Trans. Pattern Anal. Mach. Intell. 12, 966–977 (1990). [CrossRef]
  23. M. D'Zmura, " Color constancy: surface color from changing illumination," J. Opt. Soc. Am. A 9, 490–493 (1992). [CrossRef]
  24. B. Funt and G. Finlayson, "Color constant color indexing," IEEE Trans. Pattern Anal. Mach. Intell. 17, 522–529 (1995). [CrossRef]
  25. G. H. Golub and C. F. van Loan, Matrix Computations (Johns Hopkins University Press, Baltimore, Md., 1983).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited