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Proceedings Paper

Random graph representation for 3-D object models
Author(s): L. J. Bruce McArthur; Andrew K. C. Wong
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Paper Abstract

A key capability for an intelligent machine vision system is the ability to autonomously acquire new information about an environment. This is especially true in model-based 3-D object recognition, where a bottleneck exists in the generation of geometric models and the selection of suitable sets of features for recognizing each model. We describe a new model representation, based on the random graph, for use in 3-D object recognition. The random graph is a probabilistic representation of an ensemble of attributed graphs which can describe variations in both the structure and attribute values of structural patterns. The random graph is well-suited to accommodate the uncertain and incomplete nature of real-world data and is able to meet the information requirements for object recognition through the representation of feature visibility, detectability, and variability. In the random graph object model, vertices represent geometric features, such as points, edges, and planar surfaces, and arcs represent topological relations. Uncertainty in geometric feature attributes and in model structure is described by attaching probability distributions to model vertex and arc attribute values. A specific example, the point feature random graph model, has been implemented and is described in greater detail.

Paper Details

Date Published: 1 February 1992
PDF: 10 pages
Proc. SPIE 1609, Model-Based Vision Development and Tools, (1 February 1992); doi: 10.1117/12.57126
Show Author Affiliations
L. J. Bruce McArthur, Univ. of Waterloo (Canada)
Andrew K. C. Wong, Univ. of Waterloo (Canada)

Published in SPIE Proceedings Vol. 1609:
Model-Based Vision Development and Tools
Rodney M. Larson; Hatem N. Nasr, Editor(s)

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