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. 11, Iss. 5 — May. 1, 1994
  • pp: 1553–1563

Spectral sharpening: sensor transformations for improved color constancy

Graham D. Finlayson, Mark S. Drew, and Brian V. Funt  »View Author Affiliations


JOSA A, Vol. 11, Issue 5, pp. 1553-1563 (1994)
http://dx.doi.org/10.1364/JOSAA.11.001553


View Full Text Article

Enhanced HTML    Acrobat PDF (1421 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

We develop sensor transformations, collectively called spectral sharpening, that convert a given set of sensor sensitivity functions into a new set that will improve the performance of any color-constancy algorithm that is based on an independent adjustment of the sensor response channels. Independent adjustment of multiplicative coefficients corresponds to the application of a diagonal-matrix transform (DMT) to the sensor response vector and is a common feature of many theories of color constancy, Land’s retinex and von Kries adaptation in particular. We set forth three techniques for spectral sharpening. Sensor-based sharpening focuses on the production of new sensors as linear combinations of the given ones such that each new sensor has its spectral sensitivity concentrated as much as possible within a narrow band of wavelengths. Data-based sharpening, on the other hand, extracts new sensors by optimizing the ability of a DMT to account for a given illumination change by examining the sensor response vectors obtained from a set of surfaces under two different illuminants. Finally in perfect sharpening we demonstrate that, if illumination and surface reflectance are described by two- and three-parameter finite-dimensional models, there exists a unique optimal sharpening transform. All three sharpening methods yield similar results. When sharpened cone sensitivities are used as sensors, a DMT models illumination change extremely well. We present simulation results suggesting that in general nondiagonal transforms can do only marginally better. Our sharpening results correlate well with the psychophysical evidence of spectral sharpening in the human visual system.

© 1994 Optical Society of America

History
Original Manuscript: March 8, 1993
Revised Manuscript: October 28, 1993
Manuscript Accepted: October 28, 1993
Published: May 1, 1994

Citation
Graham D. Finlayson, Mark S. Drew, and Brian V. Funt, "Spectral sharpening: sensor transformations for improved color constancy," J. Opt. Soc. Am. A 11, 1553-1563 (1994)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-11-5-1553


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. J. Beck, Surface Color Perception (Cornell U. Press, Ithaca, N.Y., 1972).
  2. G. West, M. H. Brill, “Necessary and sufficient conditions for von Kries chromatic adaption to give colour constancy,” J. Math. Biol. 15, 249–258 (1982). [CrossRef]
  3. E. H. Land, J. J. McCann, “Lightness and retinex theory,” J. Opt. Soc. Am. 61, 1–11 (1971). [CrossRef] [PubMed]
  4. B. K. P. Horn, “Determining lightness from an image,” Comput. Vision Graphics Image Process. 3, 277–299 (1974). [CrossRef]
  5. A. Blake, “Boundary conditions for lightness computation in Mondrian world,” Comput. Vision Graphics Image Process. 32, 314–327 (1985). [CrossRef]
  6. D. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vision 5, 5–36 (1990). [CrossRef]
  7. M. D’Zmura, P. Lennie, “Mechanisms of color constancy,” J. Opt. Soc. Am. A 3, 1662–1672 (1986). [CrossRef]
  8. B. V. Funt, M. S. Drew, “Color constancy computation in near-Mondrian scenes using a finite dimensional linear model,” in Computer Vision and Pattern Recognition Proceedings (Institute of Electrical and Electronics Engineers, New York, 1988), pp. 544–549.
  9. M. H. Brill, “Computer-simulated object-color recognizer,” Tech. Rep. 122 (MIT Research Laboratory of Electronics, Cambridge, Mass., 1980).
  10. G. Wyszecki, W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulas, 2nd ed. (Wiley, New York, 1982).
  11. E. H. Land, “The retinex theory of color vision,” Sci. Am. 237, 108–129 (1977). [CrossRef] [PubMed]
  12. D. H. Foster, “Changes in field spectral sensitivities of red-, green- and blue-sensitive colour mechanisms obtained on small background fields,” Vision Res. 21, 1433–1455 (1981). [CrossRef] [PubMed]
  13. H. G. Sperling, R. S. Harwerth, “Red–green cone interactions in the increment-threshold spectral sensitivity of primates,” Science 172, 180–184 (1971). [CrossRef] [PubMed]
  14. M. Kalloniatis, R. S. Harwerth, “Spectral sensitivity and adaptation characteristics of cone mechanisms under white-light adaptation,” J. Opt. Soc. Am. A 7, 1912–1928 (1990). [CrossRef] [PubMed]
  15. D. C. Hood, M. A. Finkelstein, “A case for the revision of textbook models of colour vision,” in Colour Vision: Physiology and Psychophysics, J. D. Mollon, L. T. Sharpe, eds. (Academic, New York, 1983), pp. 385–398.
  16. A. B. Poirson, B. A. Wandell, “Task-dependent color discrimination,” J. Opt. Soc. Am. A 7, 776–782 (1990). [CrossRef] [PubMed]
  17. J. H. Wilkinson, Algebraic Eigenvalue Problem, Monographs on Numerical Analysis (Oxford U. Press, Oxford, 1965).
  18. J. K. Bowmaker, H. J. A. Dartnell, “Visual pigments of rods and cones in the human retina,” J. Physiol. 298, 501–511 (1980).
  19. D. B. Judd, D. L. MacAdam, G. Wyszecki, “Spectral distribution of typical daylight as a function of correlated color temperature,” J. Opt. Soc. Am. 54, 1031–1040 (1964). [CrossRef]
  20. J. Cohen, “Dependency of the spectral reflectance curves of the Munsell color chips,” Psychon. Sci. 1, 369–370 (1964).
  21. L. T. 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]
  22. C. L. Novak, S. A. Shafer, “Supervised color constancy using a color chart,” Tech. Rep. CMU-CS-90-140 (Carnegie–Mellon University School of Computer Science, Pittsburgh, Pa., 1990).
  23. G. D. Finlayson, M. S. Drew, B. V. Funt, “Enhancing von Kries adaptation via sensor transformations,” in Human Vision, Visual Processing, and Digital Display TV, J. P. Allebach, Bernice E. Roqowitz, eds., Proc. Soc. Photo-Opt. Instrum. Eng.1913, 473–484 (1993).
  24. D. H. Marimont, B. A. Wandell, “Linear models of surface and illuminant spectra,” J. Opt. Soc. Am. A 9, 1905–1913 (1992). [CrossRef] [PubMed]
  25. M. D’Zmura, “Color constancy: surface color from changing illumination,” J. Opt. Soc. Am. A 9, 490–493 (1992). [CrossRef]
  26. D. H. Foster, “Colour vision,” Contemp. Phys. 25, 477–497 (1984). [CrossRef]
  27. D. H. Foster, R. S. Snelgar, “Test and field spectral sensitivities of colour mechanisms obtained on small white backgrounds: action of unitary opponent-colour processes?” Vision Res. 23, 787–797 (1983). [CrossRef] [PubMed]
  28. D. H. Foster, R. S. Snelgar, “Initial analysis of opponent-colour interactions revealed in sharpened field sensitivities,” in Colour Vision: Physiology and Psychophysics, J. D. Mollon, L. T. Sharpe, eds. (Academic, New York, 1983), pp. 303–312.
  29. W. Jaeger, H. Krastel, S. Braun, “An incrementthreshold evaluation of mechanisms underlying colour constancy,” in Colour Vision: Physiology and Psychophysics, J. D. Mollon, L. T. Sharpe, eds. (Academic, New York, 1983), pp. 545–552.
  30. Kodak Filters: for Scientific and Technical Uses, 2nd ed. (Eastman Kodak, Rochester, N.Y., 1981).

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