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Proceedings Paper

Infrared image segmentation based on two-dimensional maximum fuzzy entropy with genetic algorithm
Author(s): Jin Wu; Juan Li; Ya Qiu; Jian Liu; Jinwen Tian
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Paper Abstract

Forward Looking Infra-Red (FLIR) image segmentation is crucial for Automatic Target Recognition (ATR). This paper presents a thresholding method for image segmentation by performing fuzzy partition on a two-dimensional (2-D) histogram based on maximum entropy principle. We combine the original image with its smooth image to form a binary set, called a "generalized image", and the histogram of the generalized image is a 2-D histogram. In order to adequately utilize the intrinsic information of the FLIR image, we adopt a newly defined fuzzy partition of two fuzzy sets, dark and bright, basing on 2-D histogram. Also we define the corresponding 2-D membership function, which represents the membership of darkness and brightness for each element in the binary set, respectively. The entropy is used as a measure of fuzziness. Based on the Shannon function, we define a 2-D fuzzy entropy. The total fuzzy entropy is the sum of the entropy of each block. Therefore, the fuzzy region can be determined by maximizing the total fuzzy entropy. A genetic algorithm is employed to find the optimal combination of all the fuzzy parameters. Experiment results show that the proposed method gives good performance.

Paper Details

Date Published: 10 January 2005
PDF: 10 pages
Proc. SPIE 5640, Infrared Components and Their Applications, (10 January 2005); doi: 10.1117/12.576530
Show Author Affiliations
Jin Wu, Wuhan Univ. of Science and Technology (China)
Juan Li, Wuhan Univ. of Science and Technology (China)
Ya Qiu, Wuhan Univ. of Science and Technology (China)
Jian Liu, Huazhong Univ. of Science and Technology (China)
Jinwen Tian, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 5640:
Infrared Components and Their Applications
Haimei Gong; Yi Cai; Jean-Pierre Chatard, Editor(s)

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