计算机工程 ›› 2009, Vol. 35 ›› Issue (3): 230-232.doi: 10.3969/j.issn.1000-3428.2009.03.078

• 图形图像处理 • 上一篇    下一篇

基于QPSO的二维模糊最大熵图像阈值分割方法

田 杰,曾建潮   

  1. (太原科技大学系统仿真与计算机应用研究所,太原 030024)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-02-05 发布日期:2009-02-05

2D Fuzzy Maximum Entropy Image Threshold Segmentation Method Based on QPSO

TIAN Jie, ZENG Jian-chao   

  1. (Institute of System Simulation & Computer Application, Taiyuan University of Science & Technology, Taiyuan 030024)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-02-05 Published:2009-02-05

摘要: 针对运用图像分割方法求取阈值时存在的计算复杂、时间长、实用性差等问题,提出一种新的二维最大熵图像分割方法,该方法利用基于量子行为的微粒群算法对图像的二维阈值空间进行全局搜索,并将搜索到的二维熵最大值所对应的点灰度-区域灰度均值作为阈值进行图像分割。实验结果表明,该方法具有一定优越性,在执行时间与收敛性方面均得到较理想的分割效果。

关键词: 图像分割, 二维模糊最大熵, 量子行为的微粒群优化算法

Abstract: Aiming at the problems such as complex calculation, long executive time, and worse practicability when using image segmentation method to seek threshold, a novel 2D maximum entropy image segmentation method is proposed, which uses Quantum-behaved Particle Swam Optimization(QPSO) algorithm to conduct global search of 2D image threshold space, and takes the gray scale value of pixel and the gray scale mean value of region corresponding to 2D maximum entropy value as the threshold for image segmentation. Experimental results show this method has some advantages in aspects of executive time and astringency.

Key words: image segmentation, 2D fuzzy maximum entropy, Quantum-behaved Particle Swam Optimization(QPSO) algorithm

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