Color models for outdoor machine vision
Computer Vision and Image Understanding
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One of the key tools in applying physics-based models to machine vision has been the analysis of color histograms. In the mid-1980s it was recognized that the color histogram for a single inhomogeneous surface with highlights will have a planar distribution in color space. It has since been shown that the colors do not fall randomly in a plane, but form clusters at specific points. The shape of the histogram is related not only to the illumination color and object color, but also to such non-color properties as surface roughness and imaging geometry. We present here an algorithm for analyzing color histograms that yields estimates of surface roughness, phase angle between the camera and light source, and illumination intensity. These three scene parameters are related to three histogram measurements. However the relationship is complex and cannot be solved analytically. Therefore we have developed a method for estimating these properties by interpolating between histograms that come from images of known scene properties. We have also shown how roughness and imaging geometry affect the recovery of illumination color from highlight analysis. Our estimates of the roughness and phase angle allow us to make a better estimate of illumination color. This in turn will give better estimates of object colors to achieve color constancy. Our method for estimating scene properties is very fast, and requires only a single color image.