Obtaining accurate color images for machine-vision research

Algorithms for color machine vision rely upon an explicit or implicit model of how color images are formed. These models usually include an idealized description of how camera systems measure color. Unfortunately, relying on these idealizations can cause serious errors in the performance of algorithms. In this paper, we describe several problems that occur in real color images and how they can be dealt with. A common problem is that many cameras do not have a linear response. Failure to correct this problem will not only cause hue shifts, but will cause problems in any algorithms that rely on the linear assumption. We show how this effect may be measured and corrected. More insidious are the problems of color clipping and blooming, since they can cause sudden changes in the apparent hue. Although it is not possible to correct the problem, we show how it may be detected so that affected measurements may be discounted. Most models assume that integration is performed over the visible spectrum, but many cameras are sensitive to other regions as well. Failure to compensate for this can lead to unexpected results. Also the camera’s varying spectral sensitivity within the visible spectrum will cause some measurements to be more prone to noise than others. We show how the use of a computer- controlled lens can compensate for this problem. Chromatic aberration in typical video camera lenses can cause hue changes, and we show how the further use of computer-controlled lens can compensate for this problem. Using these methods to detect and/or correct problems, we can obtain the accuracy needed for applying physics- based methods to actual color images.