Color estimation from a single surface color

This paper estimates illumination colors by using only a single surface color taken under multiple illumination colors. Past researchers have found that there is a difficulty in estimating illumination colors using a single surface color. However, the method presented here overcomes the problem. Surface color is estimated by considering four characteristics of illumination and surface color spaces. First, the outdoor-illumination colors exist in a specific color range. Second, multiple illuminations give constraints for the surface color. Third, multiple illuminations also give constraints for the color range. Fourth, each color component affects those constraints in a different manner. Based on those characteristics, a novel method can be designed. The proposed method produces consistently accurate results when multiple illumination colors are used, because the constraint (possible range of illumination colors) on illumination colors refines the estimated illumination colors, effectively.

[1]  Peter Shirley,et al.  A practical analytic model for daylight , 1999, SIGGRAPH.

[2]  J. Marchant,et al.  Shadow-invariant classification for scenes illuminated by daylight. , 2000, Journal of the Optical Society of America. A, Optics, image science, and vision.

[3]  G D Finlayson,et al.  Color constancy at a pixel. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

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

[5]  Martial Hebert,et al.  Color constancy using KL-divergence , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[6]  P. Lennie,et al.  Mechanisms of color constancy: erratum , 1986 .

[7]  Steven A. Shafer,et al.  Supervised color constancy for machine vision , 1991, Electronic Imaging.

[8]  Poorvi L. Vora,et al.  Digital color cameras - 2 - Spectral response , 1997 .

[9]  Katsushi Ikeuchi,et al.  Consistent surface color for texturing large objects in outdoor scenes , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[10]  Theo Gevers,et al.  Color Constancy using Natural Image Statistics , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Joost van de Weijer,et al.  Author Manuscript, Published in "ieee Transactions on Image Processing Edge-based Color Constancy , 2022 .

[12]  K. Ikeuchi,et al.  Color constancy from blackbody illumination. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[13]  D H Brainard,et al.  Bayesian color constancy. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[14]  K. Ikeuchi,et al.  Color constancy through inverse-intensity chromaticity space. , 2004, Journal of the Optical Society of America. A, Optics, image science, and vision.

[15]  David A. Forsyth,et al.  A novel algorithm for color constancy , 1990, International Journal of Computer Vision.

[16]  E. Land.  The retinex theory of color vision. , 1977, Scientific American.

[17]  Graham D. Finlayson,et al.  Color by Correlation: A Simple, Unifying Framework for Color Constancy , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  B. Wandell,et al.  Standard surface-reflectance model and illuminant estimation , 1989 .

[19]  Katsushi Ikeuchi,et al.  Stabilizing Illumination Chromaticity Estimation Using the Illumination Line Segment , 2007, MVA.

[20]  Shree K. Nayar,et al.  Radiometric self calibration , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[21]  B. Wandell,et al.  Natural scene-illuminant estimation using the sensor correlation , 2002, Proc. IEEE.

[22]  Steven D. Hordley,et al.  Scene illuminant estimation: Past, present, and future , 2006 .

[23]  Ingeborg Tastl,et al.  Gamut Constrained Illuminant Estimation , 2006, International Journal of Computer Vision.

[24]  J. Marchant,et al.  Spectral invariance under daylight illumination changes. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[25]  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.

[26]  Brian V. Funt,et al.  Color constancy under varying illumination , 1995, Proceedings of IEEE International Conference on Computer Vision.

[27]  J. Michalsky,et al.  All-weather model for sky luminance distribution—Preliminary configuration and validation , 1993 .

[28]  K Barnard,et al.  Sensor sharpening for computational color constancy. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.