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Proceedings Paper

Statistical Segmentation Of Digital Images
Author(s): Jay B. Jordan; G. M. Flachs
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Paper Abstract

Statistically based models of digital images are used to locate and segment objects of interest from background scenes. Three models are presented and evaluated. These models are based on a Bayesian cost function, a Neyman-Pearson constant false alarm rate function, and a maximum entropy function. Detailed algorithms are presented for separating object regions from background clutter using each of these statistical methods.

Paper Details

Date Published: 21 August 1987
PDF: 9 pages
Proc. SPIE 0754, Optical and Digital Pattern Recognition, (21 August 1987); doi: 10.1117/12.939988
Show Author Affiliations
Jay B. Jordan, New Mexico State University (United States)
G. M. Flachs, New Mexico State University (United States)

Published in SPIE Proceedings Vol. 0754:
Optical and Digital Pattern Recognition
Hua-Kuang Liu; Paul S. Schenker, Editor(s)

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