Object segmentation using ant colony optimization algorithm and fuzzy entropy

Authors:
Wenbing Tao;Hai Jin;Liman Liu
Affiliations:
Cluster and Grid Computing Laboratory, School of Computer, Huazhong University of Science and Technology, Wuhan 430074, China and Service Computing Technology and System Laboratory of Ministry of ...;Cluster and Grid Computing Laboratory, School of Computer, Huazhong University of Science and Technology, Wuhan 430074, China and Service Computing Technology and System Laboratory of Ministry of ...;O2 Micro, Wuhan 430074, China
Venue:
Pattern Recognition Letters
Year:
2007

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Abstract

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).