MODELS OF STATISTICAL VISUAL-MOTION ESTIMATION

Several models of statistical estimation of motion from visual input a re derived and analyzed theoretically and experimentally. We study a w ide variety of models, ones that use least squares and ones that use m aximum likelihood, with several different assumptions (dependent and i ndependent noise, isotropic and nonisotropic noise), spherical and pla nar image surfaces, and different preprocessing (one based on correspo ndence and one based on disparity). We do all this analysis using only a few fundamental concepts from statistical estimation, so the relati ve merits and shortcomings of all the methods become evident. The expe rimental results provide a quantitative measure of these merits. (C) 1 994 Academic Press, Inc.