智能优化算法在图像分割中的性能分析  

Performance Analysis of Intelligent Optimization Algorithms in Image Thresholding Segmentation

作　　者：刘娜[1] 姜燕[1] 

机构地区：[1]湖北医药学院公共管理学院,十堰442000

出　　处：《计算机与数字工程》2013年第5期808-811,共4页Computer & Digital Engineering

基　　金：湖北医药学院研究生启动金计划项目(编号:2011QDZR-15)资助

摘　　要：在阈值分割算法中,确定最优阈值是图像分割的关键。但阈值的选取大多采用穷尽的搜索方式,运算效率较低,抗噪能力不强,容易产生误分割。针对这些问题,考虑采用智能优化算法来搜寻最优阈值,旨在最大限度地提高寻优效率和寻优精度。微粒群算法和蚂蚁算法是具有代表性的仿生优化算法,将它们实施于图像分割的应用领域,对微粒群算法和蚂蚁算法的阈值分割效果进行了比较分析,实验数据表明,微粒群算法更容易实现,在寻优阈值和运行时间方面取得了更好效果。In image segmentation algorithms,the selection of optimal threshold is the key to segmentation.However,the most of threshold selection methods adopt the mode of exhaustive search so that the operation efficiency is low,the capability of noise resisting is weak,and error segmentation happens easily in these methods.To solve the above problems,this paper adopts intelligent optimization algorithms to search for the optimal threshold,aiming to maximize the efficiency and accuracy.Particle swarm optimization algorithm and ant Colony algorithm are representative algorithms of the intelligent optimization algorithms,the effect of image segmentation between them are analysed and compared,the experimental data show that the particle swarm algorithm are easier to implement,in terms of optimal threshold and running time has obtained the better effect.

关 键 词：阈值分割 微粒群算法 蚂蚁算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]