Share Email Print

Proceedings Paper

Rapid Recognition Out Of A Large Model Base Using Prediction Hierarchies And Machine Parallelism
Author(s): J.Brian Burns; Leslie J. Kitchen
Format Member Price Non-Member Price
PDF $17.00 $21.00
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

An object recognition system is presented to handle the computational complexity posed by a large model base, an unconstrained viewpoint, and the structural complexity and detail inherent in the projection of an object. The design is based on two ideas. The first is to compute descriptions of what the objects should look like in the im-age, called predictions, before the recognition task begins. This reduces actual recognition to a 2D matching process, speeding up recognition time for 3D objects. The second is to represent all the predictions by a single, combined IS-A and PART-OF hierarchy called a prediction hierarchy. The nodes in this hierarchy are partial descriptions that are common to views and hence constitute shared processing subgoals during matching. The recognition time and storage demands of large model bases and complex models are substantially reduced by subgoal sharing: projections with similarities explicitly share the recognition and representation of their common aspects. A prototype system for the automatic compilation of a prediction hierarchy from a 3D model base is demonstrated using a set of polyhedral objects and projections from an unconstrained range of viewpoints. In addition, the adaptation of prediction hierarchies for use on the UMass Image Understanding Architecture is considered. Object recognition using prediction hierar-chies can naturally exploit the hierarchical parallelism of this machine.

Paper Details

Date Published: 19 February 1988
PDF: 9 pages
Proc. SPIE 0848, Intelligent Robots and Computer Vision VI, (19 February 1988); doi: 10.1117/12.942740
Show Author Affiliations
J.Brian Burns, University of Massachusetts (United States)
Leslie J. Kitchen, University of Western Australia (Australia)

Published in SPIE Proceedings Vol. 0848:
Intelligent Robots and Computer Vision VI
David P. Casasent; Ernest L. Hall, Editor(s)

© SPIE. Terms of Use
Back to Top