Color and Illuminant Voting

A geometric-vision approach to color constancy and illuminant estimation is presented in this paper. We show a general framework, based on ideas from the generalized probabilistic Hough transform, to estimate the illuminant and reflectance of natural images. Each image pixel "votes" for possible illuminants and the estimation is based on cumulative votes. The framework is natural for the introduction of physical constraints in the color constancy problem. We show the relationship of this work to previous algorithms for color constancy and present examples.

[1]  G. Healey,et al.  Illumination-invariant recognition of texture in color images , 1995 .

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

[3]  A. Jepson,et al.  The computation of color constant descriptors in chromatic images , 1989 .

[4]  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.

[5]  Ronen Basri,et al.  Recognition by Linear Combinations of Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  O. Faugeras.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

[7]  Doron Shaked,et al.  Deriving Stopping Rules for the Probabilistic Hough Transform by Sequential Analysis , 1996, Comput. Vis. Image Underst..

[8]  Graham D. Finlayson,et al.  Color constancy in diagonal chromaticity space , 1995, Proceedings of IEEE International Conference on Computer Vision.

[9]  Graham D. Finlayson,et al.  Color by Correlation , 1997, CIC.

[10]  Michael Werman,et al.  The study of 3D-from-2D using elimination , 1995, Proceedings of IEEE International Conference on Computer Vision.

[11]  J. Cohen.  Dependency of the spectral reflectance curves of the Munsell color chips , 1964 .

[12]  Joshua B. Tenenbaum,et al.  Learning bilinear models for two-factor problems in vision , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

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

[15]  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.

[16]  B. Buchberger,et al.  Grobner Bases : An Algorithmic Method in Polynomial Ideal Theory , 1985 .

[17]  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.

[18]  M. D'Zmura,et al.  Color constancy. I. Basic theory of two-stage linear recovery of spectral descriptions for lights and surfaces. , 1993, Journal of the Optical Society of America. A, Optics, image science, and vision.

[19]  B. Wandell,et al.  Black light: how sensors filter spectral variation of the illuminant , 1989, IEEE Transactions on Biomedical Engineering.

[20]  E H Land,et al.  COLOR VISION AND THE NATURAL IMAGE PART II. , 1959, Proceedings of the National Academy of Sciences of the United States of America.

[21]  G D Finlayson,et al.  Spectral sharpening: sensor transformations for improved color constancy. , 1994, Journal of the Optical Society of America. A, Optics, image science, and vision.

[22]  H. J. Trussell,et al.  Estimation of illumination for color correction , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[23]  M. D'Zmura,et al.  Color constancy. II. Results for two-stage linear recovery of spectral descriptions for lights and surfaces. , 1993, Journal of the Optical Society of America. A, Optics, image science, and vision.

[24]  M D'Zmura,et al.  Color constancy. III. General linear recovery of spectral descriptions for lights and surfaces. , 1994, Journal of the Optical Society of America. A, Optics, image science, and vision.

[25]  A. Shashua.  Geometry and Photometry in 3D Visual Recognition , 1992 .

[26]  William T. Freeman,et al.  Bayesian decision theory, the maximum local mass estimate, and color constancy , 1995, Proceedings of IEEE International Conference on Computer Vision.

[27]  D. B. Judd,et al.  Spectral Distribution of Typical Daylight as a Function of Correlated Color Temperature , 1964 .

[28]  Guillermo Sapiro.  Bilinear voting , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[29]  Berthold K. P. Horn,et al.  Determining lightness from an image , 1974, Comput. Graph. Image Process..

[30]  S. Ullman,et al.  Geometry and photometry in three-dimensional visual recognition , 1993 .