Color Constancy with Specular and Non-Specular Surfaces
There is a growing trend in machine color constancy research to use only image chromaticity information, ignoring the magnitude of the image pixels. This is natural because the main purpose is often to estimate only the chromaticity of the illuminant. However, the magnitudes of the image pixels also carry information about the chromaticity of the illuminant. One such source of information is through image specularities. As is well known in the computational color constancy field, specularities from inhomogeneous materials (such as plastics and painted surfaces) can be used for color constancy. This assumes that the image contains specularities, that they can be identified, and that they do not saturate the camera sensors. These provisos make it important that color constancy algorithms which make use of specularities also perform well when the they are absent. A further problem with using specularities is that the key assumption, namely that the specular component is the color of the illuminant, does not hold in the case of colored metals. In this paper we investigate a number of color constancy algorithms in the context of specular and nonspecular reflection. We then propose extensions to several variants of ForsythOs CRULE algorithm [1-4] which make use of specularities if they exist, but do not rely on their presence. In addition, our approach is easily extended to include colored metals, and is the first color constancy algorithm to deal with such surfaces. Finally, our method provides an estimate of the overall brightness, which chromaticity-based methods cannot do, and other RGB based algorithms do poorly when specularities are present.