Recognizing volumetric objects in the presence of uncertainty

Abstract

This paper describes a new framework for parametric shape recognition. The key result is a method for generating classifiers in the form of conditional probability densities for recognizing an unknown from a set of reference models. The authors' procedure is automatic. Off-line, it invokes an autonomous process to estimate reference model parameters and their statistics. On-line, during measurement, it combines these with a priori context-dependent information, as well as the parameters and statistics estimated for an unknown object, into a conditional probability density function, which represents the belief that the unknown is a particular reference model. The paper also describes the implementation of this procedure in a system for automatically generating and recognizing 3-D part-oriented models. The authors show that recognition performance is near perfect for cases in which complete surface information is accessible to the algorithm, and that it falls off gracefully when only partial information is available. This leads to the possibility of an active recognition strategy in which the belief measures associated with each classification can be used as feedback for the acquisition of further evidence as required.