期刊
PATTERN RECOGNITION LETTERS
卷 28, 期 7, 页码 788-796出版社
ELSEVIER
DOI: 10.1016/j.patrec.2006.11.007
关键词
infrared object segmentation; ant colony optimization; fuzzy entropy; performance evaluation; global thresholding
In this paper, we investigate the performance of the fuzzy entropy approach when it is applied to the segmentation of infrared objects. Through a number of examples, the performance is compared with those using existing entropy-based object segmentation approaches and the superiority of the fuzzy entropy method is demonstrated. In addition, the ant colony optimization (ACO) is used to obtain the optimal parameters. The experiment results show that, compared with the genetic algorithm (GA), the implementation of the proposed fuzzy entropy method incorporating with the ACO provides improved search performance and requires significantly reduced computations. Therefore, it is suitable for real-time vision applications, such as automatic target recognition (ATR). (c) 2006 Elsevier B.V. All rights reserved.
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