A truncated least squares approach to the detection of specular highlights in color images

One of the most difficult aspects of dealing with illumination effects in computer vision is accounting for specularity in the images of real objects. The specular regions in an image are often saturated-which creates problem for all image processing algorithms that use decision thresholds. Such algorithms include those for edge detection, region segmentation, etc. Detecting specularity and whenever possible compensating for it are obviously advantageous. Along these lines, this paper represents a new specularity detection and compensation method which is based on the notion of truncated least-squares approximation to the function that maps the color distribution between two images of an object under different illumination conditions. We also present a protocol for the evaluation of the current method for specularity detection. Our protocol as currently formulated uses human subjects to grade the specularity detection method.