Probabilistic geometric grammars for object recognition

Abstract

This thesis presents a generative three-dimensional (3D) representation and recognition framework for classes of objects. The framework uses probabilistic grammars to represent object classes recursively in terms of their parts, thereby exploiting the hierarchical and substitutive structure inherent to many types of objects. The framework models the 3) geometric characteristics of object parts using multivariate conditional Gaussians over dimensions, position, and rotation. I present algorithms for learning geometric models and rule probabilities given parsed 3D examples and a fixed grammar. I also present a parsing algorithm for classifying unlabeled, unparsed 3D examples given a geometric grammar. Finally, I describe the results of a set of experiments designed to investigate the chosen model representation of the framework.