Probabilistic IR modeling for Bayesian automatic object recognition

Rufus H. Cofer; Samuel Peter Kozaitis

doi:10.1117/12.160595

15 October 1993 Probabilistic IR modeling for Bayesian automatic object recognition

Rufus H. Cofer; Samuel Peter Kozaitis

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Proceedings Volume 1960, Automatic Object Recognition III; (1993) https://doi.org/10.1117/12.160595
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

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Rufus H. Cofer, Rufus H. Cofer, Samuel Peter Kozaitis, Samuel Peter Kozaitis, } "Probabilistic IR modeling for Bayesian automatic object recognition", Proc. SPIE 1960, Automatic Object Recognition III, (15 October 1993); doi: 10.1117/12.160595; https://doi.org/10.1117/12.160595

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