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Object segmentation using ant colony optimization algorithm and fuzzy entropy
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DOI：10.1016/j.patrec.2006.11.007.png)
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Abstract
En 中文
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.
Keywords:
infrared object segmentation
ant colony optimization
fuzzy entropy
performance evaluation
global thresholding
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