Abstract This thesis describes a new framework for parametric shape recognition. The key result is a method for generating classi ers in the form of conditional probability densities for recognizing an unknown from a set of reference models. Our procedure is automatic. O line, it invokes an autonomous process to estimate reference model parameters and their statistics. On-line, during measureme...