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Color in Perspective
October 1996 (vol. 18 no. 10)
pp. 1034-1038

Abstract—Simple constraints on the sets of possible surface reflectances and illuminants are exploited in a new color constancy algorithm that builds upon Forsyth's [1] theory of color constancy. Forsyth's method invokes the constraint that the surface colors under a canonical illuminant all fall within an established maximal convex gamut of possible colors. Unfortunately the method works only when restrictive conditions are imposed on the world: the illumination must be uniform, the surfaces must be planar, and there can be no specularities. To overcome these restrictions, we modify Forsyth's algorithm so that it works with the colors under a perspective projection (in essence in a chromaticity space). The new algorithm working in perspective is simpler than Forsyth's (its computational complexity is reduced) and more importantly the restrictions on the illuminant, surface shape and specularities can be relaxed. The algorithm is then extended to include a maximal gamut constraint on the set of illuminants that is analogous to the gamut constraint on surface colors. Tests on real images show that the algorithm provides good color constancy.

[1] K. Barnard, "Computational Color Constancy: Taking Theory Into Practice," MSc thesis, Simon Fraser Univ., School of Computing Science, 1995.
[2] R.S. Berns and K.H. Petersen, "Empirical Modelling of Systematic Spectrophotometric Errors," Color Res. Appl., vol. 4, p. 243, 1988.
[3] G.J. Brelstaff, "Inferring Surface Shape From Specular Reflections," PhD thesis, Univ. of Edinburgh, Dept. of Computer Science, 1989.
[4] G.D. Finlayson, "Color in Perspective," Technical Report CSS/LCCR TR 95, Simon Fraser Univ., School of Computing Science, 1995.
[5] G.D. Finlayson, M.S. Drew, and B.V. Funt, "Spectral Sharpening: Sensor Transformations for Improved Color Constancy," J. Opt. Soc. Am., vol. A11, no. 5, pp. 1,553-1,563, May 1994.
[6] J.D. Foley and A.V. Dam,Fundamentals of Interactive Computer Graphics.Reading, Mass.: Addison-Wesley, 1982.
[7] D.A. Forsyth, “A Novel Algorithm for Color Constancy,” Int'l J. Computer Vision, vol. 5, no. 1, pp. 5-36, 1990.
[8] D.B. Judd, D.L. MacAdam, and G. Wyszecki, "Spectral Distribution of Typical Daylight as a Function of Correlated Color Temperature," J. Opt. Soc. Am., vol. 54, pp. 1,031-1,040, Aug. 1964.
[9] L.T. Maloney and B.A. Wandell, "Color Constancy: A Method for Recovering Surface Spectral Reflectance," J. Opt. Soc. Am., vol. A3, pp. 29-33, 1986.
[10] F.P. Preparata and M.I. Shamos, Computational Geometry. Springer-Verlag, 1985.
[11] S.A. Shafer, "Using Color to Separate Reflection Components," Color Res. Appl., vol. 10, pp. 210-218, 1985.
[12] G. Sharma and H.J. Trussel, "Characterization of Scanner Sensitivity," IS&T and SID's Color Imaging Conf.: Transforms&Transportability of Color, pp. 103-107, 1993.
[13] G. Wyszecki and W.S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulas.New York: Wiley, 1982, 2nd edition.

Index Terms:
Color, color constancy, physics-based vision.
G.d. Finlayson, "Color in Perspective," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 10, pp. 1034-1038, Oct. 1996, doi:10.1109/34.541413
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