Abstract: This paper describes a new framework for parametric shape recognition based on a probabilistic model of inverse theory first introduced by Tarantola. 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. Our 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 apriori context-dependent information, as well as the parameters and statistics estimated for an unknown object, into a single description. That description, a conditional probability density function, represents the likelihood of correspondence between the unknown and 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. Specifically we 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 (minimal false-positive response) 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.