FORMS: a flexible object recognition and modelling system

We briefly describe a generic statistical framework for representing the shapes of animate objects using principal component analysis and stochastic shape grammars. Such a representation scheme gives a formalism for solving the inverse problem-object recognition. Then we show: how these representations can be extracted from 2D silhouettes by a novel method for skeleton extraction and shape segmentation; how a similarity metric can be defined on this shape space; and how we can perform recognition in a bottom up/top down loop. The system is demonstrated to be stable in the presence of noise, the absence of parts, the presence of additional parts, and considerable variations in articulation and viewpoint. Successful categorization is demonstrated on a dataset of seventeen categories of animate objects