Partial shape recognition: a landmark-based approach

A method of recognizing partially occluded objects is presented in which each object is represented by a set of landmarks. Given a scene consisting of partially occluded objects, a model object in the scene is hypothesized by matching the landmarks of the model with those in the scene. A measure of similarity between two landmarks is needed to perform this matching. A local shape measure, sphericity, is introduced. It is shown that any invariant function under a similarity transformation is a function of the sphericity. To match landmarks between the model and the scene, a table of compatibility is constructed. A technique, known as hopping dynamic programming, is described to guide the landmark matching through the compatibility table. The location of the model in the scene is estimated with a least-squares fit among the matched landmarks. A heuristic measure is then computed to decide if the model is in the scene