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Optical Engineering

Improved forward-looking IR image segmentation using stochastic image models
Author(s): Chee Sun Won; Jinwoo Park
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Paper Abstract

We propose an improved segmentation algorithm to extract an object from a forward-looking infrared (FLIR) image. The observed FLIR images are considered to be made up of three stochastic models. The first model is in charge of the noise component and is assumed to be independent Gaussian. The labeling of two regions (i.e., the object and the background) in the second model should obey the Gibbs random field (GRF). Finally, we adopt a population parameter to represent the ratio of the size of the object to that of the background. The population parameter eases the tendency to produce similar-sized segmentations. Establishing the stochastic models, we incorporate maximum a posteriori (MAP) estimation to determine the region labels. The optimization of the MAP criterion is achieved by a deterministic relaxation method to converge quickly to a local maximum.

Paper Details

Date Published: 1 May 1994
PDF: 4 pages
Opt. Eng. 33(5) doi: 10.1117/12.164609
Published in: Optical Engineering Volume 33, Issue 5
Show Author Affiliations
Chee Sun Won, Dongguk Univ. (South Korea)
Jinwoo Park, Korea Univ. (South Korea)

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