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Proceedings Paper

Object recognition using appearance representations derived from solid models of objects
Author(s): Michael A. Sipe; David P. Casasent
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Paper Abstract

We advance active computer vision algorithms for flexible manufacturing systems that classify objects and estimate their pose from intensity images. Our algorithms automatically reposition the sensor if the class or pose of an object is ambiguous in a given image and incorporate data from multiple object views in determining the final object classification. A feature space trajectory (FST) in a global eigenfeature space is used to represent 3-D distorted views of an object. Bayesian methods are used to derive the class hypothesis, pose estimate, confidence measures, and the sensor position that best resolves ambiguity. FSTs constructed using images rendered from solid models of objects are used to process real image data.

Paper Details

Date Published: 6 July 1998
PDF: 12 pages
Proc. SPIE 3387, Visual Information Processing VII, (6 July 1998); doi: 10.1117/12.316398
Show Author Affiliations
Michael A. Sipe, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 3387:
Visual Information Processing VII
Stephen K. Park; Richard D. Juday, Editor(s)

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