Color Constancy through Inverse-Intensity Chromaticity Space
Existing color constancy methods cannot handle both uniformly colored surfaces and highly textured surfaces in a single integrated framework. Statistics-based methods require many surface colors, and become error prone when there are only a few surface colors. In contrast, dichromatic-based methods can successfully handle uniformly colored surfaces, but cannot be applied to highly textured surfaces since they require precise color segmentation. In this chapter, we present a single integrated method to estimate illumination chromaticity from single-colored and multi-colored surfaces. Unlike existing dichromatic-based methods, our proposed method requires only rough highlight regions, without segmenting the colors inside them. We show that, by analyzing highlights, a direct correlation between illumination chromaticity and image chromaticity can be obtained. This correlation is clearly described in “inverse-intensity chromaticity space”, a novel two-dimensional space we introduce. In addition, by utilizing the Hough transform and histogram analysis in this space, illumination chromaticity can be estimated robustly, even for a highly textured surface.
KeywordsOptical Society Surface Color Color Constancy Green Channel Blue Channel
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- D’Zmura, M., Lennie,P., "Mechanism of color constancy", Journal of the Optical Society of America A, 3(10), pp. 1162-1672, 1986.Google Scholar
- G.D. Finlayson, B.V. Funt , "Color constancy using shadows", Perception 23, pp. 89-90, 1994.Google Scholar
- G.D. Finlayson and G. Schaefer, "Convex and non-convex illumination constraints for dichromatic color constancy", in proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 598-605, 2001.Google Scholar
- J.M. Geusebroek, R. Boomgaard, S. Smeulders, T. Gevers, "A physical basis for color constancy", in proceeding of The First European Conference on Colour in Graphics, Image and Vision, pp. 3-6, 2002.Google Scholar
- G.J. Klinker , Shafer, S.A., Kanade, T., "The measurement of highlights in color images", International Journal of Computer Vision, 2, pp 7-32, 1990.Google Scholar
- G.J. Klinker , "A physical approach to color image understanding", PhD. Thesis, Carnegie Mellon University, May, 1988.Google Scholar
- J.H. Lambert , "Photometria sive de mensura de gratibus luminis, colorum et umbrae", Eberhard Klett: Augsberg, Germany, 1760.Google Scholar
- H.C. Lee, "Illuminant color from shading", in Physics-based Vision Principle and Practice: Color, pp. 340-347, 1992, Jones and Bartlett.Google Scholar
- C. Rosenberg, M. Hebert, S. Thrun, "Color constancy using KL-Divergence", in proceeding of IEEE International Conference on Computer Vision, pp.239-247, 2001.Google Scholar
- G. Wyszecki, W.S. Stiles, "Color science: concept and methods, quantitative data and formulae",Wiley Inter-Science,1982.Google Scholar