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Colour constancy for scenes with varying illumination

  • Kobus Barnard
  • Graham Finlayson
  • Brian Funt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1065)

Abstract

We present an algorithm which uses information from both surface reflectance and illumination variation to solve for colour constancy. Most colour constancy algorithms assume that the illumination across a scene is constant, but this is very often not valid for real images. The method presented in this work identifies and removes the illumination variation, and in addition uses the variation to constrain the solution. The constraint is applied conjunctively to constraints found from surface reflectances. Thus the algorithm can provide good colour constancy when there is sufficient variation in surface reflectances, or sufficient illumination variation, or a combination of both. We present the results of running the algorithm on several real scenes, and the results are very encouraging.

Keywords

Surface Reflectance Colour Constancy Surface Constraint Spectral Power Distribution Incident Illumination 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    A. Blake, “Boundary conditions for lightness computation in Mondrian world”, Computer Vision, Graphics, and Image Processing, 32, pp. 314–327, (1985)Google Scholar
  2. 2.
    G. Buchsbaum, “A spatial processor model for object colour perception”, Journal of the Franklin Institute, 310, pp. 1–26, (1980)Google Scholar
  3. 3.
    W. Freeman and David Brainard, “Bayesian Decision Theory, the Maximum Local Mass Estimate, and Color Constancy”, in Proceedings: Fifth International Conference on Computer Vision, pp 210–217, (IEEE Computer Society Press, 1995)Google Scholar
  4. 4.
    B. V. Funt, M. S. Drew, M. Brockington, “Recovering Shading from Color Images”, in Proceedings: Second European Conference on Computer Vision, G. Sandini, ed., 1992.Google Scholar
  5. 5.
    G.D. Finlayson and M.S. Drew and B.V. Funt, “Spectral Sharpening: Sensor Transformations for Improved Color Constancy”, J. Opt. Soc. Am. A, 11, 5, pp. 1553–1563, (1994)Google Scholar
  6. 6.
    G.D. Finlayson and M.S. Drew and B.V. Funt, “Color Constancy. Generalized Diagonal Transforms Suffice”, J. Opt. Soc. Am. A, 11, 11, pp. 3011–3020, (1994)Google Scholar
  7. 7.
    G. D. Finlayson, B. V. Funt, and K. Barnard, “Color Constancy Under Varying Illumination”, in Proceedings: Fifth International Conference on Computer Vision, pp 720–725, 1995.Google Scholar
  8. 8.
    G. D. Finlayson, “Color Constancy in Diagonal Chromaticity Space”, in Proceedings: Fifth International Conference on Computer Vision, pp 218–223, (IEEE Computer Society Press, 1995).Google Scholar
  9. 9.
    D. Forsyth, “A novel algorithm for color constancy”, Int. J. Computer. Vision, 5, pp. 5–36, (1990)Google Scholar
  10. 10.
    R. Gershon and A.D. Jepson and J.K. Tsotsos, “Ambient illumination and the determination of material changes”, J. Opt. Soc. Am. A, 3, 10, pp. 1700–1707, (1986)Google Scholar
  11. 11.
    B.K.P. Horn, “Determining lightness from an image”, Computer Vision, Graphics, and Image Processing, 3, pp. 277–299, (1974)Google Scholar
  12. 12.
    D.B. Judd and D.L. MacAdam and G. Wyszecki, “Spectral Distribution of Typical Daylight as a Function of Correlated Color Temperature”, J. Opt. Soc. Am., 54, 8, pp. 1031–1040, (August 1964)Google Scholar
  13. 13.
    E.H. Land, “The Retinex theory of Color Vision”, Scientific American, 108–129, (1977)Google Scholar
  14. 14.
    John J. McCann, Suzanne P. McKee, and Thomas H. Taylor, “Quantitative Studies in Retinex Theory”, Vision Research, 16, pp. 445–458, (1976)Google Scholar
  15. 15.
    L.T. Maloney and B.A. Wandell, “Color constancy: a method for recovering surface spectral reflectance”, J. Opt. Soc. Am. A, 3, 1, pp. 29–33, (1986)Google Scholar
  16. 16.
    M. Tsukada and Y. Ohta, “An Approach to Color Constancy Using Multiple Images”, in Proceedings Third International Conference on Computer Vision, (IEEE Computer Society, 1990)Google Scholar
  17. 17.
    G. Wyszecki and W.S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulas, 2nd edition, (Wiley, New York, 1982)Google Scholar
  18. 18.
    M. D'Zmura and G. Iverson, “Color constancy. I. Basic theory of two-stage linear recovery of spectral descriptions for lights and surfaces”, J. Opt. Soc. Am. A, 10, 10, pp. 2148–2165, (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Kobus Barnard
    • 1
  • Graham Finlayson
    • 2
  • Brian Funt
    • 1
  1. 1.School of Computing ScienceSimon Fraser UniversityBurnabyCanada
  2. 2.Department of Computer ScienceUniversity of YorkYorkUK

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