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Proceedings Paper

Model-based object classification using unification grammars and abstract representations
Author(s): Kathleen A. Liburdy; Robert J. Schalkoff
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Paper Abstract

The design and implementation of a high level computer vision system which performs object classification is described. General object labelling and functional analysis require models of classes which display a wide range of geometric variations. A large representational gap exists between abstract criteria such as `graspable' and current geometric image descriptions. The vision system developed and described in this work addresses this problem and implements solutions based on a fusion of semantics, unification, and formal language theory. Object models are represented using unification grammars, which provide a framework for the integration of structure and semantics. A methodology for the derivation of symbolic image descriptions capable of interacting with the grammar-based models is described and implemented. A unification-based parser developed for this system achieves object classification by determining if the symbolic image description can be unified with the abstract criteria of an object model. Future research directions are indicated.

Paper Details

Date Published: 20 April 1993
PDF: 14 pages
Proc. SPIE 1827, Model-Based Vision, (20 April 1993); doi: 10.1117/12.143071
Show Author Affiliations
Kathleen A. Liburdy, Clemson Univ. (United States)
Robert J. Schalkoff, Clemson Univ. (United States)

Published in SPIE Proceedings Vol. 1827:
Model-Based Vision
Hatem N. Nasr; Rodney M. Larson, Editor(s)

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