A novel algorithm for color constancy

AbstractColor constancy is the skill by which it is possible to tell the color of an object even under a colored light. I interpret the color of an object as its color under a fixed canonical light, rather than as a surface reflectance function. This leads to an analysis that shows two distinct sets of circumstances under which color constancy is possible. In this framework, color constancy requires estimating the illuminant under which the image was taken. The estimate is then used to choose one of a set of linear maps, which is applied to the image to yield a color descriptor at each point. This set of maps is computed in advance.The illuminant can be estimated using image measurements alone, because, given a number of weak assumptions detailed in the text, the color of the illuminant is constrained by the colors observed in the image. This constraint arises from the fact that surfaces can reflect no more light than is cast on them. For example, if one observes a patch that excites the red receptor strongly, the illuminant cannot have been deep blue.Two algorithms are possible using this constraint, corresponding to different assumptions about the world. The first algorithm, Crule will work for any surface reflectance. Crule corresponds to a form of coefficient rule, but obtains the coefficients by using constraints on illuminant color. The set of illuminants for which Crule will be successful depends strongly on the choice of photoreceptors: for narrowband photoreceptors, Crule will work in an unrestricted world. The second algorithm, Mwext, requires that both surface reflectances and illuminants be chosen from finite dimensional spaces; but under these restrictive conditions it can recover a large number of parameters in the illuminant, and is not an attractive model of human color constancy. Crule has been tested on real images of Mondriaans, and works well. I show results for Crule and for the Retinex algorithm of Land (Land 1971; Land 1983; Land 1985) operating on a number of real images. The experimental work shows that for good constancy, a color constancy system will need to adjust the gain of the receptors it employs in a fashion analagous to adaptation in humans.

[1]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[2]  Nikolaus Nyberg Zum Aufbau des Farbenkörpers im Raume aller Lichtempfindungen , 1929 .

[3]  D B JUDD,et al.  Appraisal of Land's work on two-primary color projections. , 1960, Journal of the Optical Society of America.

[4]  Deane B. Judd Appraisal of Land's work on two-primary color projections. , 1960 .

[5]  R. Eisenschitz Matrix Algebra for Physicists , 1966 .

[6]  W D Wright,et al.  Color Science, Concepts and Methods. Quantitative Data and Formulas , 1967 .

[7]  Gunther Wyszecki,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition , 2000 .

[8]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

[9]  J. Beck Surface color perception , 1972 .

[10]  W. Boothby An introduction to differentiable manifolds and Riemannian geometry , 1975 .

[11]  S. McKee,et al.  Quantitative studies in retinex theory a comparison between theoretical predictions and observer responses to the “color mondrian” experiments , 1976, Vision Research.

[12]  W. Stiles,et al.  Counting metameric object-color stimuli using frequency-limited spectral reflectance functions , 1977 .

[13]  M. H. Brill,et al.  A device performing illuminant-invariant assessment of chromatic relations. , 1978, Journal of theoretical biology.

[14]  Philip C. Parr,et al.  Beam alignment technique using linear arrays (A) , 1979 .

[15]  W. Stiles,et al.  High-level trichromatic color matching and the pigment-bleaching hypothesis , 1980, Vision Research.

[16]  G. Buchsbaum A spatial processor model for object colour perception , 1980 .

[17]  M. H. Brill,et al.  Contributions to the theory of invariance of color under the condition of varying illumination , 1981 .

[18]  M. H. Brill,et al.  Necessary and sufficient conditions for Von Kries chromatic adaptation to give color constancy , 1982, Journal of mathematical biology.

[19]  K. M. Quinlan,et al.  Reducing the effect of complexity on volume model evaluation , 1982 .

[20]  E H Land,et al.  Recent advances in retinex theory and some implications for cortical computations: color vision and the natural image. , 1983, Proceedings of the National Academy of Sciences of the United States of America.

[21]  A. Gilchrist,et al.  The classification and integration of edges as critical to the perception of reflectance and illumination , 1983, Perception & psychophysics.

[22]  J. A. Worthey Limitations of color constancy , 1985 .

[23]  Andrew Blake,et al.  Boundary conditions for lightness computation in Mondrian World , 1985, Comput. Vis. Graph. Image Process..

[24]  E. Land Recent advances in retinex theory , 1986, Vision Research.

[25]  H C Lee,et al.  Method for computing the scene-illuminant chromaticity from specular highlights. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[26]  M D'Zmura,et al.  Mechanisms of color constancy. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[27]  D H Brainard,et al.  Analysis of the retinex theory of color vision. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[28]  L. Maloney,et al.  Color constancy: a method for recovering surface spectral reflectance. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[29]  M. H. Brill,et al.  Heuristic analysis of von Kries color constancy. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[30]  L. Maloney Evaluation of linear models of surface spectral reflectance with small numbers of parameters. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[31]  John K. Tsotsos,et al.  Ambient illumination and the determination of material changes. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[32]  K Sasaki,et al.  Component analysis of spatial and spectral patterns in multispectral images. I. Basis. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[33]  Jan J. Koenderink,et al.  Color atlas theory , 1987 .

[34]  Brian A. Wandell,et al.  The Synthesis and Analysis of Color Images , 1992, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Andrew Blake,et al.  Computing lightness , 1987, Pattern Recognit. Lett..

[36]  R. Gershon The use of color in computational vision , 1987 .

[37]  Mark S. Drew,et al.  Color constancy computation in near-Mondrian scenes using a finite dimensional linear model , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[38]  David A. Forsyth,et al.  Colour constancy and its applications in machine vision , 1988 .

[39]  Gavin J. Brelstaff,et al.  Inferring surface shape from specular reflections , 1988 .

[40]  Stephen Cameron Efficient Intersection Tests for Objects Defined Constructively , 1989, Int. J. Robotics Res..

[41]  David A. Forsyth,et al.  Shape from shading in the light of mutual illumination , 1990, Image Vis. Comput..

[42]  M. J. Luque,et al.  Implementations of a novel algorithm for colour constancy , 1997, Vision Research.

[43]  K. Hohn,et al.  Determining Lightness from an Image , 2004 .

[44]  Mark S. Drew,et al.  Color constancy from mutual reflection , 1991, International Journal of Computer Vision.