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. 21, Iss. 3 — Mar. 1, 2004
  • pp: 321–334

Color constancy through inverse-intensity chromaticity space

Robby T. Tan, Ko Nishino, and Katsushi Ikeuchi  »View Author Affiliations


JOSA A, Vol. 21, Issue 3, pp. 321-334 (2004)
http://dx.doi.org/10.1364/JOSAA.21.000321


View Full Text Article

Enhanced HTML    Acrobat PDF (835 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Existing color constancy methods cannot handle both uniformly colored surfaces and highly textured surfaces in a single integrated framework. Statistics-based methods require many surface colors and become error prone when there are only a few surface colors. In contrast, dichromatic-based methods can successfully handle uniformly colored surfaces but cannot be applied to highly textured surfaces, since they require precise color segmentation. We present a single integrated method to estimate illumination chromaticity from single-colored and multicolored surfaces. Unlike existing dichromatic-based methods, the proposed method requires only rough highlight regions without segmenting the colors inside them. We show that, by analyzing highlights, a direct correlation between illumination chromaticity and image chromaticity can be obtained. This correlation is clearly described in “inverse-intensity chromaticity space,” a novel two-dimensional space that we introduce. In addition, when Hough transform and histogram analysis is utilized in this space, illumination chromaticity can be estimated robustly, even for a highly textured surface.

© 2004 Optical Society of America

OCIS Codes
(150.0150) Machine vision : Machine vision
(150.2950) Machine vision : Illumination

History
Original Manuscript: April 21, 2003
Revised Manuscript: September 15, 2003
Manuscript Accepted: November 4, 2003
Published: March 1, 2004

Citation
Robby T. Tan, Ko Nishino, and Katsushi Ikeuchi, "Color constancy through inverse-intensity chromaticity space," J. Opt. Soc. Am. A 21, 321-334 (2004)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-21-3-321


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. G. F. Finlayson, G. Schaefer, “Solving for colour constancy using a constrained dichromatic reflection model,” Int. J. Comput. Vision 42, 127–144 (2001). [CrossRef]
  2. D. H. Brainard, W. T. Freeman, “Bayesian color constancy,” J. Opt. Soc. Am. A 14, 1393–1411 (1997). [CrossRef]
  3. G. D. Finlayson, “Color in perspective,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 1034–1036 (1996). [CrossRef]
  4. G. D. Finlayson, S. D. Hordley, P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209–1221 (2001). [CrossRef]
  5. C. Rosenberg, M. Hebert, S. Thrun, “Color constancy using KL-divergence,” in Proceedings of the IEEE International Conference on Computer Vision (Institute of Electrical and Electronics Engineers, New York, 2001), pp. 239–247.
  6. G. Sapiro, “Color and Illumination Voting,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 1210–1215 (1999). [CrossRef]
  7. S. Tominaga, S. Ebisui, B. A. Wandell, “Scene illuminant classification: brighter is better,” J. Opt. Soc. Am. A 18, 55–64 (2001). [CrossRef]
  8. S. Tominaga, B. A. Wandell, “Natural scene-illuminant estimation using the sensor correlation,” Proc. IEEE 90, 42–56 (2002). [CrossRef]
  9. M. D’Zmura, P. Lennie, “Mechanism of color constancy,” J. Opt. Soc. Am. A 3, 1162–1672 (1986).
  10. G. D. Finlayson, B. V. Funt, “Color constancy using shadows,” Perception 23, 89–90 (1994).
  11. B. V. Funt, M. Drew, J. Ho, “Color constancy from mutual reflection,” Int. J. Comput. Vis. 6, 5–24 (1991). [CrossRef]
  12. H. C. Lee, “Method for computing the scene-illuminant from specular highlights,” J. Opt. Soc. Am. A 3, 1694–1699 (1986). [CrossRef] [PubMed]
  13. H. C. Lee, “Illuminant color from shading,” in Physics-Based Vision Principle and Practice: Color (Jones and Bartlett, Boston, Mass., 1992), pp. 340–347.
  14. G. F. Finlayson, S. D. Hordley, “Color constancy at a pixel,” J. Opt. Soc. Am. A 18, 253–264 (2001). [CrossRef]
  15. J. M. Geusebroek, R. Boomgaard, S. Smeulders, T. Gevers, “A physical basis for color constancy,” in Proceedings ofthe First European Conference on Colour in Graphics, Image and Vision (Society for Imaging Science and Technology, Springfield, Va., 2002), pp. 3–6.
  16. J. M. Geusebroek, R. Boomgaard, S. Smeulders, H. Geert, “Color invariance,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1338–1350 (2001). [CrossRef]
  17. H. J. Andersen, E. Granum, “Classifying illumination conditions from two light sources by colour histogram assessment,” J. Opt. Soc. Am. A 17, 667–676 (2000). [CrossRef]
  18. S. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210–218 (1985). [CrossRef]
  19. G. J. Klinker, S. A. Shafer, T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vis. 2, 7–32 (1990). [CrossRef]
  20. G. J. Klinker, “A physical approach to color image understanding,” PhD. thesis (Carnegie Mellon University, Pittsburgh, Pa., 1988).
  21. S. Tominaga, “A multi-channel vision system for estimating surface and illumination functions,” J. Opt. Soc. Am. A 13, 2163–2173 (1996). [CrossRef]
  22. S. Tominaga, B. A. Wandell, “Standard surface-reflectance model and illumination estimation,” J. Opt. Soc. Am. A 6, 576–584 (1989). [CrossRef]
  23. T. M. Lehmann, C. Palm, “Color line search for illuminant estimation in real-world scene,” J. Opt. Soc. Am. A 18, 2679–2691 (2001). [CrossRef]
  24. G. D. Finlayson, G. Schaefer, “Convex and non-convex illumination constraints for dichromatic color constancy,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, New York, 2001), pp. 598–605.
  25. G. Healey, “Using color for geometry-insensitive segmentation,” J. Opt. Soc. Am. A 6, 920–937 (1989). [CrossRef]
  26. H. C. Lee, E. J. Breneman, C. P. Schulte, “Modeling light reflection for computer color vision,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 402–409 (1990). [CrossRef]
  27. J. H. Lambert, Photometria sive de mensura de gratibus luminis, colorum et umbrae (Eberhard Klett, Augsberg, Germany, 1760).
  28. K. E. Torrance, E. M. Sparrow, “Theory for off-specular reflection from roughened surfaces,” J. Opt. Soc. Am. 57, 1105–1114 (1967). [CrossRef]

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.


Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited