Statistical approaches to feature-based object recognition

This paper examines statistical approaches to model-based object recognition. Evidence is presented indicating that, in some domains, normal (Gaussian) distributions are more accurate than uniform distributions for modeling feature fluctuations. This motivates the development of new maximum-likelihood and MAP recognition formulations which are based on normal feature models. These formulations lead to an expression for the posterior probability of the pose and correspondences given an image. Several avenues are explored for specifying a recognition hypothesis. In the first approach, correspondences are included as a part of the hypotheses. Search for solutions may be ordered as a combinatorial search in correspondence space, or as a search over pose space, where the same criterion can equivalently be viewed as a robust variant of chamfer matching. In the second approach, correspondences are not viewed as being a part of the hypotheses. This leads to a criterion that is a smooth function of pose that is amenable to local search by continuous optimization methods. The criteria is also suitable for optimization via the Expectation-Maximization (EM) algorithm, which alternates between pose refinement and re-estimation of correspondence probabilities until convergence is obtained. Recognition experiments are described using the criteria with features derived from video images and from synthetic range images.