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基于改进粒子群算法的多阈值图像分割
引用本文:武燕,张冰.基于改进粒子群算法的多阈值图像分割[J].微型电脑应用,2011,27(5):59-61,70.
作者姓名:武燕  张冰
作者单位:江苏省常州市武进区,212003
摘    要:提出了一种改进的粒子群算法,在初始化种群时采用相对基学习原理,以获得较优的初始候选解;在后期迭代过程中引入扩张模型,使粒子不易陷入局部极小值点,并将其用于多阈值图像分割。由最大熵阈值法得到所要优化的目标函数,用改进的粒子群算法对其进行优化,使其能够准确并迅速的得到分割的最佳阈值组合,并用该阈值组合对图像进行分割。将此分割结果与遗传算法的多阈值分割结果相比较可以看出,该算法可更为准确快速的实现图像分割。

关 键 词:粒子群优化算法  相对基学习  扩张模型  多阈值  图像分割

Multilevel Thresholding Methodsfor Image Segmentation Based on Improved Particle Swarm Optimization
Wu Yan,Zhang Bing.Multilevel Thresholding Methodsfor Image Segmentation Based on Improved Particle Swarm Optimization[J].Microcomputer Applications,2011,27(5):59-61,70.
Authors:Wu Yan  Zhang Bing
Affiliation:Wu Yan,Zhang Bing (Electronic information faculty,Jiangsu University of Science and Technology,212003 Jiangsu,China)
Abstract:To determine the optimal thresholds in image segmentation,an improved particle swarm optimization(pso) is put forward. In this method, it adopted Opposition-Based Learning in initialization to get a better solution and adopted expansion model in later iteration to avoid getting into local minumum.The optimization object function using maximum entropy(ME)method can be gotten. By the optimization of the object function, the optimal thresholds can be gotten well and quickly, and the image by use of the thresho...
Keywords:Particle Swarm Optimization  Opposition-Based Learning  Expansion Model  Multilevel Thresholding  Image Segmentation  
本文献已被 CNKI 维普 万方数据 等数据库收录!
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