10 January 2005 Infrared image segmentation based on two-dimensional maximum fuzzy entropy with genetic algorithm
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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.
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Jin Wu, Juan Li, Ya Qiu, Jian Liu, Jinwen Tian, "Infrared image segmentation based on two-dimensional maximum fuzzy entropy with genetic algorithm", Proc. SPIE 5640, Infrared Components and Their Applications, (10 January 2005); doi: 10.1117/12.576530; https://doi.org/10.1117/12.576530
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