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Proceedings Paper

Probabilistic IR modeling for Bayesian automatic object recognition
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Paper Abstract

Based upon the requirements of Bayesian object recognition theory, this paper provides the fundamental framework to determine the joint probability density function of object regions in an IR image. This probability function contains all information about the region that is required to achieve minimum probability of error recognition. The techniques advanced here are expected to be of significant use in certain rather hostile and difficult situations such as testing piping for fault conditions within operational nuclear power plants.

Paper Details

Date Published: 15 October 1993
PDF: 7 pages
Proc. SPIE 1960, Automatic Object Recognition III, (15 October 1993); doi: 10.1117/12.160595
Show Author Affiliations
Rufus H. Cofer, Florida Institute of Technology (United States)
Samuel Peter Kozaitis, Florida Institute of Technology (United States)


Published in SPIE Proceedings Vol. 1960:
Automatic Object Recognition III
Firooz A. Sadjadi, Editor(s)

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