| 基于微粒子群理论的二维最大类间方差阈值分割算法 |
| |
| 引用本文： | 张俊宝,陈红林.基于微粒子群理论的二维最大类间方差阈值分割算法[J].弹箭与制导学报,2009,29(3). |
| |
| 作者姓名： | 张俊宝 陈红林 |
| |
| 作者单位： | 西北工业大学电子信息学院,西安,710072 |
| |
| 摘 要： | 针对传统二维最大类间方差(Otsu)阈值分割算法处理红外图像耗时的缺点,提出了一种应用微粒子群理论的二维Otsu阈值分割算法.该算法利用粒子群理论的群体智能的特点,通过优化得出粒子的个体极值和全局极值,并根据这两种极值来更新粒子的位置和速度以获得最佳的分割阈值向量.通过对算法中惯性权重和学习因子的讨论确定了最佳的参数选择方案.仿真结果表明,该算法计算准确,流程简单,其运行时间仅为原始算法的5%左右,是一种快速有效的图像阈值分割算法.
|
| 关 键 词： | 图像分割 二维 算法 微粒子群算法 |
The Two-dimensional Maximum Between-cluster Variance Threshold Segmentation Algorithm Basing on Particle Swarm Optimization |
| |
| ZHANG Junbao,CHEN Honglin.The Two-dimensional Maximum Between-cluster Variance Threshold Segmentation Algorithm Basing on Particle Swarm Optimization[J].Journal of Projectiles Rockets Missiles and Guidance,2009,29(3). |
| |
| Authors: | ZHANG Junbao CHEN Honglin |
| |
| Affiliation: | ZHANG Junbao,CHEN Honglin(School of Electronics , Information,Northwestern Polytechnical University,Xi'an 710072,China) |
| |
| Abstract: | The traditional two-dimension maximum between-cluster variance algorithm is always time-consuming in processing infrared image.A kind of using particle swarm optimization in two-dimension maximum between-cluster variance algorithm is proposed.This algorithm,using the character of community intelligence,obtains the individual extremum and overall extremum,then,according to the two extremums to update the particle's position and velocity to get the best division threshold vector.The discussion of inertia weig... |
| |
| Keywords: | Otsu |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
|
