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结合粒子群算法优化归一割的图像阈值分割方法
引用本文:任爱红.结合粒子群算法优化归一割的图像阈值分割方法[J].西北纺织工学院学报,2012(3):337-341.
作者姓名:任爱红
作者单位:宝鸡文理学院数学系,陕西宝鸡721013
基金项目:陕西省教育厅科研计划项目(11JK0506);宝鸡文理学院科研计划项目(YK1026)
摘    要:为了快速得到图像分割的最佳阈值,依据图论知识,利用灰度级相似矩阵代替像素级权值矩阵,将归一化切割准则作为优化函数.利用粒子群优化算法代替穷举法优化归一化划分准则,提出粒子群算法优化归一割的图像阈值分割方法.实验表明在分割性能上有较大的提高,在分割速度上也有较大的改进,能够满足实时性要求.

关 键 词:阈值分割  归一化割  粒子群算法  图像分割

Image threshold segmentation approach of normalized cut and particle swarm optimization algorithm
REN Ai-hong.Image threshold segmentation approach of normalized cut and particle swarm optimization algorithm[J].Journal of Northwest Institute of Textile Science and Technology,2012(3):337-341.
Authors:REN Ai-hong
Affiliation:REN Ai-hong(Department of Mathematics,Baoji University of Arts and Sciences,Baoji,Shaanxi 721013,China)
Abstract:In order to get the optimal threshold in image segmentation quickly,based on the graph theory,gray-scale similar matrix takes the place of pixel-level weight matrix,normalized cut criterion is regarded as the optimization function.Using particle swarm optimization algorithm to find the best threshold in gray-scale space.Experiments show that the method is not only less computational costs,but also get a satisfactory segmentation result.The thresholds is more stable and consume less time greatly and better satisfies the request of real-time processing in image segmentation by using this new method.
Keywords:threshold segmentation  normalized cut  particle swarm algorithm  image segmentation
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