Color constancy in a rough world

This article introduces a new psychophysical method for a performance-based view of color constancy, in which the task for the observer is to identify similar materials across illuminants despite possible appearance changes, and to simultaneously extract the relative colors of the illuminants.15 The article also examines generality conditions for the task. Physical and neural constraints on chromatic signals make it possible to use simple affine-heuristic algorithms to solve the correspondence problem for most Lambertian surfaces in random spatial arrangements under different illuminants. For rough surfaces, where the relative amounts of interface and body reflections vary with source-object-sensor geometry, the algorithms solve the correspondence problem across illuminants for a constant source-object-sensor geometry, but are not successful for rough surfaces in different spatial arrangements under different illuminants. © 2000 John Wiley & Sons, Inc. Col Res Appl, 26, S192–S200, 2001

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

[2]  Q Zaidi,et al.  Identification of illuminant and object colors: heuristic-based algorithms. , 1998, Journal of the Optical Society of America. A, Optics, image science, and vision.

[3]  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.

[4]  Ron Gershon,et al.  Measurement and Analysis of Object Reflectance Spectra , 1994 .

[5]  D. Foster,et al.  Relational colour constancy from invariant cone-excitation ratios , 1994, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[6]  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.

[7]  Qasim Zaidi,et al.  Lateral interactions within color mechanism in simultaneous induced contrast , 1992, Vision Research.

[8]  Marcel P. Lucassen,et al.  Testing the Contrast Explanation of Color Constancy , 1991 .

[9]  J L Dannemiller,et al.  Computational approaches to color constancy: adaptive and ontogenetic considerations. , 1989, Psychological review.

[10]  K. Torrance,et al.  Theory for off-specular reflection from roughened surfaces , 1967 .

[11]  A. Valberg,et al.  “Colour constancy” in Mondrian patterns: A partial cancellation of physical chromaticity shifts by simultaneous contrast , 1990, Vision Research.

[12]  P. Lennie,et al.  Chromatic mechanisms in lateral geniculate nucleus of macaque. , 1984, The Journal of physiology.

[13]  A. H. Taylor,et al.  The Distribution of Energy in the Visible Spectrum of Daylight , 1941 .

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

[15]  D. W. Heeley,et al.  Cardinal directions of color space , 1982, Vision Research.

[16]  Michael H. Brill,et al.  The relation between the color of the illuminant and the color of the illuminated object , 1995 .

[17]  Shree K. Nayar,et al.  Reflectance and texture of real-world surfaces , 1999, TOGS.

[18]  M. S. Drew,et al.  Color constancy - Generalized diagonal transforms suffice , 1994 .

[19]  M H Brill,et al.  Image segmentation by object color: a unifying framework and connection to color constancy. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[20]  L. Arend,et al.  Simultaneous color constancy. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[21]  J. Pokorny,et al.  Spectral sensitivity of the foveal cone photopigments between 400 and 500 nm , 1975, Vision Research.

[22]  D. Brainard,et al.  Color constancy in the nearly natural image. 2. Achromatic loci. , 1998, Journal of the Optical Society of America. A, Optics, image science, and vision.

[23]  L. B. Wolff Diffuse-reflectance model for smooth dielectric surfaces , 1994 .

[24]  R. M. Boynton,et al.  Chromaticity diagram showing cone excitation by stimuli of equal luminance. , 1979, Journal of the Optical Society of America.

[25]  Q Zaidi,et al.  Color constancy in variegated scenes: role of low-level mechanisms in discounting illumination changes. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[26]  A C Hurlbert,et al.  Measurements of Colour Constancy by Using a Forced-Choice Matching Technique , 1996, Perception.

[27]  Q Zaidi,et al.  Decorrelation of L- and M-cone signals. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[28]  James L. Dannemiller,et al.  Rank orderings of photoreceptor photon catches from natural objects are nearly illuminant-invariant , 1993, Vision Research.

[29]  J. Rieger,et al.  Sensory and cognitive contributions of color to the recognition of natural scenes , 2000, Current Biology.

[30]  W L Sachtler,et al.  Chromatic and luminance signals in visual memory. , 1992, Journal of the Optical Society of America. A, Optics and image science.

[31]  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.