Random graph representation for 3-D object models

L. J. Bruce McArthur; Andrew K. C. Wong

doi:10.1117/12.57126

1 February 1992 Random graph representation for 3-D object models

L. J. Bruce McArthur, Andrew K. C. Wong

Proceedings Volume 1609, Model-Based Vision Development and Tools; (1992) https://doi.org/10.1117/12.57126
Event: Robotics '91, 1991, Boston, MA, United States

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.

Citation Download Citation

L. J. Bruce McArthur and Andrew K. C. Wong "Random graph representation for 3-D object models", Proc. SPIE 1609, Model-Based Vision Development and Tools, (1 February 1992); https://doi.org/10.1117/12.57126

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available