计算机工程与应用 ›› 2010, Vol. 46 ›› Issue (10): 175-176.DOI: 10.3778/j.issn.1002-8331.2010.10.055

• 图形、图像、模式识别 • 上一篇    下一篇

基于混沌粒子群算法的多阈值图像分割

蒋艳会,李 峰   

  1. 长沙理工大学 计算机与通信工程学院,长沙 410076
  • 收稿日期:2008-09-25 修回日期:2008-12-26 出版日期:2010-04-01 发布日期:2010-04-01
  • 通讯作者: 蒋艳会

Multi-threshold method of image segmentation based on chaotic particle swarm optimization algorithm

JIANG Yan-hui,LI Feng   

  1. Computer and Communication Engineering Institute,Changsha University of Science & Technology,Changsha 410076,China
  • Received:2008-09-25 Revised:2008-12-26 Online:2010-04-01 Published:2010-04-01
  • Contact: JIANG Yan-hui

摘要: 针对单阈值图像分割方法在求取比较复杂的图像时效果不理想及粒子群算法容易陷入局部最优且速度较慢等等问题,提出了基于混沌粒子群优化算法的多阈值图像分割方法。该方法利用混沌运动随机性、遍历性和初值敏感性,将混沌粒子群优化算法与多阈值法相结合作全局搜索,实验结果表明了基于混沌粒子群优化算法的多阈值图像分割法用于阈值寻优减少了搜索时间,并且运行时间不随阈值数目的增加而显著增加。

关键词: 图像分割, 粒子群优化算法, 多阈值, 混沌

Abstract: Due to the problems of the single threshold image segmentation method isn’t ideal,the particle swarm optimization algorithm is easy to fall into local optimum,and the speed is slow,a multi-threshold method of image segmentation based on chaotic particle swarm optimization algorithm is proposed to solve the optimization problems.By using randomicity,ergodic and initial value sensitivity of chaos,chaotic particle swarm optimization algorithm is combined with multi-threshold method.The experimental result indicates multi-threshold method of image segmentation based on chaotic particle swarm optimization algorithm reduces the searching time,and the operating time doesn’t significantly enhance with the increase number of threshold.

Key words: image segmentation, particle swarm optimization algorithm, multi-threshold, chaos

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