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Proceedings Paper

Statistical models for target detection in infrared imagery
Author(s): Samuel H. Huddleston; Xin Zhou; William B. Evans; Alice Chan; Michael D. DeVore
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Paper Abstract

This paper illustrates a statistical model-based approach to the problem of target detection in a cluttered scene from long-wave infrared images, accommodating both unknown range to the target, unknown target location in the image, and unknown gain control settings on the imaging device. The philosophical perspective adopted emphasizes an iterative process of model creation and refinement and subsequent evaluation. The overarching theme is on the clear statement of all assumptions regarding the relationships between ground truth and corresponding imagery, the assurance that each admits quantifiable refutation, and the opportunity costs associated with their adoption for a particular problem.

Paper Details

Date Published: 7 May 2007
PDF: 11 pages
Proc. SPIE 6566, Automatic Target Recognition XVII, 65661A (7 May 2007); doi: 10.1117/12.747148
Show Author Affiliations
Samuel H. Huddleston, Univ. of Virginia (United States)
Xin Zhou, Univ. of Virginia (United States)
William B. Evans, Univ. of Virginia (United States)
Alice Chan, Univ. of Virginia (United States)
Michael D. DeVore, Univ. of Virginia (United States)

Published in SPIE Proceedings Vol. 6566:
Automatic Target Recognition XVII
Firooz A. Sadjadi, Editor(s)

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