International Journal of Computer Vision

, Volume 20, Issue 3, pp 187-212

First online:

FORMS: A flexible object recognition and modelling system

  • Song Chun ZhuAffiliated withDivision of Applied Science, Harvard University
  • , Alan L. YuilleAffiliated withDivision of Applied Science, Harvard University

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access


We describe a flexible object recognition and modelling system (FORMS) which represents and recognizes animate objects from their silhouettes. This consists of a model for generating the shapes of animate objects which gives a formalism for solving the inverse problem of object recognition. We model all objects at three levels of complexity: (i) the primitives, (ii) the mid-grained shapes, which are deformations of the primitives, and (iii) objects constructed by using a grammar to join mid-grained shapes together. The deformations of the primitives can be characterized by principal component analysis or modal analysis. When doing recognition the representations of these objects are obtained in a bottom-up manner from their silhouettes by a novel method for skeleton extraction and part segmentation based on deformable circles. These representations are then matched to a database of prototypical objects to obtain a set of candidate interpretations. These interpretations are verified in a top-down process. 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. Finally, we describe how such a representation scheme can be automatically learnt from examples.