量子粒子群算法在图像分割中的应用  

Image segmentation using Quantum-behaved Partical Swarm Optimization algorithm

作　　者：高浩[1] 须文波[1] 孙俊[1] 

机构地区：[1]江南大学信息工程学院,江苏无锡214032

出　　处：《计算机工程与应用》2007年第33期24-25,40,共3页Computer Engineering and Applications

基　　金：国家自然科学基金(the National Natural Science Foundation of China under Grant No.60474030)

摘　　要：引入了一种广泛而实用的方法——基于量子行为的粒子群算法的理论应用于图像分割领域。QPSO算法不仅参数个数少,随机性强,并且能覆盖所有解空间,保证算法的全局收敛性。文章中把图像分割问题看成一个最优化问题,以最小误差法为例,对比了所提算法和标准粒子群算法在阈值处理中的性能,并用实验证明了所提算法的可行性。An opening and practical solution,that is Quantum-behaved Particle Swarm Optimization algorithm,is applied to image segmentation.Not only parameters of QPSO is few and randomicity of QPSO is strong,but also QPSO cover with all the space of solution and guarantee global convergence of algorithms.In this paper by selecting the minimum error as an experimental method, compare the QPSO＇s ability in threshold-computation with the standard PSO,experiment results show that this method can completely satisfy the accuracy and speed＇s requirement of the real-time system and provide a better effect on image segmentation.

关 键 词：图像分割 量子粒子群算法 阈值 最小误差法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]