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

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.

Citation Download Citation

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

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