Identification of Partially Obscured Objects in Two and Three Dimensions by Matching Noisy Characteristic Curves

Abstract

In this paper we analyze the problem of matching partially obscured, noise-corrupted images of composite scenes in two and three dimensions. We describe efficient methods for smoothing the noisy data and for matching portions of the observed object boundaries (or of characteristic curves lying on bounding surfaces of 3-D objects) to prestored models. We also report initial experiments showing the efficacy of this procedure.