期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 37, 期 6, 页码 4580-4592出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2009.12.050
关键词
Image thresholding; Cross entropy; Particle swarm optimization; Honey bee mating optimization; Quantum particle swarm optimization
类别
资金
- National Science Council, ROC [NSC 97-2221-E-251-001]
Image thresholding is an important technique for image processing and pattern recognition. Many thresholding techniques have been proposed in the literature. Among them, the minimum cross entropy thresholding (MCET) has been widely applied. In this paper, a new multilevel MCET algorithm based on the technology of the honey bee mating optimization (HBMO) is proposed. Three different methods included the exhaustive search, the particle swarm optimization (PSO) and the quantum particle swarm optimization (QPSO) methods are also implemented for comparison with the results of the proposed method. The experimental results manifest that the proposed HBMO-based MCET algorithm can efficiently search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. In comparison with the other two thresholding methods, the segmentation results using the HBMO-based MCET algorithm is the best. Furthermore, the convergence of the HBMO-based MCET algorithm can rapidly achieve, and the results are validated that the proposed HBMO-based MCET algorithm is efficient. (C) 2009 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据