METHOD FOR ESTIMATING SCENE PARAMETERS FROM COLOR HISTOGRAMS

One of the key tools in applying physics-based models to machine vision has been the analysis of color histograms. In the mid-1980's 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. Physics-based models of reflection predict that the shape of the histogram is related not only to the illumination color and the object color but also to such noncolor properties as surface roughness and imaging geometry. We present an algorithm for analyzing color histograms that yields estimates of surface roughness, phase angle between the camera and the 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 developed a method for estimating these properties that is based on interpolation between histograms that come from images of known scene properties. We present tests of our algorithm on simulated data, and the results compare well with the known simulation parameters. We also test our method on real images, and the results compare favorably with the actual parameters estimated by other means. Our method for estimating scene properties is very fast and requires only a single color image.