最大类间方差法是图像分割中一种常用的阈值分割方法, 对于单阈值分割具有显著的效果, 但是对于

多阈值分割, 计算复杂度大、耗时较多。本文将粒子群优化算法与最大类间方差法结合, 提出了一种新的图像分

割方法, 该方法利用粒子群优化算法的寻优高效性, 并由灰度图像的最大类间方差值作为适应值, 搜索最优分割

阈值, 实现图像的多阈值分割。实验结果显示, 新方法大大缩短了寻找最优阈值的时间, 降低了运算复杂度, 提

高了图像分割速度, 说明基于粒子群优化算法的图像分割算法是可行的、有效的。-Maximum between-class variance method is a popular image segmentation threshold segmentation method has a significant effect for the single-threshold segmentation, but

The multi-threshold segmentation, the computational complexity of large, time-consuming. In this paper, particle swarm optimization combined with maximum between-class variance method, a new image

Cut method, the method using particle swarm optimization algorithm optimizing the efficiency of the maximum between-class variance by the gray-scale image as the fitness value, search for the optimal split

Threshold, multi-threshold image segmentation. The experimental results show that the new method greatly shorten the time to find the optimal threshold, reducing the computational complexity, to mention

The high speed of image segmentation, image segmentation algorithm based on particle swarm optimization algorithm is feasible and effective.