改进PSO算法在二维最佳阈值图像分割中的应用

摘要

According to large amount of calculation, Particle Swarm Optimization(PSO) might easily fall into local optimal values and long runtime in two dimension entropy image segmentation,this paper proposes a two dimension entropy segmentation of improved PSO.This method is based on PSO enlightened by geese floclcUsing the ideas in literature, the velocity formula is improved, and the random interfere strategy is introduced to accelerate the converge speed and overcome the part optimal values problem from two aspects.The simulations show that the searching time is shorten, the convergence speed is improved and the real-time effect of image segmentation in threshold optimization is improved too.%针对二维熵图像分割在求取最佳阈值时存在计算量大及粒子群算法容易陷入局部最优、运算速度慢等问题,提出了改进的粒子群优化算法的二维熵图像分割方法.该方法是在雁群启示的粒子群算法基础上,对速度公式进行改进,并引入随机扰动策略,从两个方面同时改进以提高算法的收敛速度,以及克服局部极值的能力.仿真结果表明,将该方法用于阈值寻优减少了搜索时间,提高了收敛速度,强化了图像处理的实时性.