CAD-based computer vision: from CAD models to relational graphs
Flynn, P.J.
Jain, A.K.
Dept. of Comput. Sci., Michigan State Univ., East Lansing, MI;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Feb 1991
Volume: 13,
Issue: 2
On page(s): 114-132
ISSN: 0162-8828
References Cited: 65
CODEN: ITPIDJ
INSPEC Accession Number: 3880503
Digital Object Identifier: 10.1109/34.67642
Current Version Published: 2002-08-06
Abstract
The topic of model-building for 3-D objects is examined. Most 3-D
object recognition systems construct models either manually or by
training. Neither approach has been very satisfactory, particularly in
designing object recognition systems which can handle a large number of
objects. Recent interest in integrating mechanical CAD systems and
vision systems has led to a third type of model building for vision:
adaptation of preexisting CAD models of objects for recognition. If a
solid model of an object to be recognized is already available in a
manufacturing database, then it should be possible to infer
automatically a model appropriate for vision tasks from the
manufacturing model. Such a system has been developed. It uses 3-D
object descriptions created on a commercial CAD system and expressed in
both the industry-standard IGES form and a polyhedral approximation and
performs geometric inferencing to obtain a relational graph
representation of the object which can be stored in a database of models
for object recognition. Relational graph models contain both
view-independent information extracted from the IGES description and
view-dependent information (patch areas) extracted from synthetic views
of the object. It is argued that such a system is needed to efficiently
create a large database (more than 100 objects) of 3-D models to
evaluate matching strategies
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