Partial Shape Recognition: A Landmark-Based Approach

When objects are occluded, many shape recognition methods that use global information will fail. To recognize partially occluded objects, we represent each object by a set of “landmarks.” The landmarks of an object are points of interest relative to the object that have important shape attributes. 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, one from the model and the other from the scene, is needed to perform this matching. In this correspondence we introduce a new local shape measure, sphericity. It will be 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, where each entry in the table is the sphericity value derived from the mapping of a set of three model landmarks to a set of three scene landmarks, 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.