Share Email Print

Proceedings Paper

Part hierarchies of object shape for recognition
Author(s): Stoyanka D. Zlateva; Marie-Christine Jaulent
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Theoretical studies of visual form perception have proposed hierarchical representations of three dimensional shape as a basis for achieving fast and reliable recognition of a wide range of objects. In this article we relate an earlier developed representation of object shape to two different types of recognition processes: (1) recognition of familiar objects, and, (2) recognition of the possible uses or affordances of not necessarily known objects in actions. The representation is based on the connectedness and neighborhood relations of object shape. It consists of three hierarchy levels of parts, sub-parts and surface patches, which build topologies of increasing strength. Each level has an associated set of qualitative and quantitative features. We submit that the visual knowledge needed for recognizing a known object is made explicit primarily at the part level and knowledge about affordances at the sub- part level. Recognition of the possible uses of objects is treated as finding the compatibility between action requirements and object affordances. The possibility and necessity of fuzzy sets are used as measures for the compatibility of the individual requirement-affordance pairs and their aggregation to the overall compatibility of a given object and action.

Paper Details

Date Published: 10 October 1994
PDF: 9 pages
Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); doi: 10.1117/12.188885
Show Author Affiliations
Stoyanka D. Zlateva, Boston Univ. (United States)
Marie-Christine Jaulent, Hopital Broussais (France)

Published in SPIE Proceedings Vol. 2353:
Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision
David P. Casasent, Editor(s)

© SPIE. Terms of Use
Back to Top