基于粒子群算法的图像分割方法研究  

Research on Image Segmentation Method Based on Particle Swarm Optimization Algorithm

作　　者：刘笃晋[1] 

机构地区：[1]四川文理学院计算机科学系,达州635000

出　　处：《现代计算机（中旬刊）》2013年第8期12-15,共4页Modern Computer

基　　金：四川文理学院研究基金(No.2012Z001Y)

摘　　要：图像分割是图像处理领域的经典难题,也是数字图像处理领域的热点问题,至今也没找到通用的图像分割方法,也没制定出一个通用的判断图像分割优劣的标准,由于粒子群算法的二维最大类间方差方法在图像分割领域有其自身的优势,目前成为研究的热点。为进一步提高图像分割效果,提高图像处理质量,对基于标准粒子群算法、基于量子粒子群算法和基于小生境粒子群算法这三种典型的基于粒子群的图像分割方法进行研究,研究结果表明,由于小生境粒子群算法的划分小生境方法,保持种群的多样性,分割效果最好,这也说明要寻找最优阈值,必须运用多样性的方法来寻找,为以后图像分割研究指明方向。Image segmentation is a classic problem in image processing field, is also a hot issue in the field of digital image processing, has not found general image segmentation method, also did not develop a general judgment criterion for image segmentation, two-dimensional maximum particle swarm algorithm between variance method in the field of image segmentation has its own advantages at present, has become a hotspot of research. In order to further improve the image segmentation effect, improve the quality of image processing, based on the standard particle swarm algorithm, based on the quantum particle swarm algorithm, based on Niching Particle swarm algorithm,which based on three typical image particle swarm segmentation methods are studied, results show that, due to divide the niching method Niching Particle swarm algorithm, to maintain the diversity of population, the segmentation effect is best, this also shows to find the optimal threshold method, must use diversity to find, for the future research direction of image segmentation.

关 键 词：图像分割 粒子群优化算法 二维阈值 小生境粒子群 量子粒子群 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]