基于快速二维熵与PSO算法的红外图像分割  

A Fast Infrared Image Segmentation Method Based on Two-Dimensional Entropy and Particle Swarm Optimization Algorithm

作　　者：刘羿彤[1] 付梦印[1] 

机构地区：[1]北京理工大学信息科学技术学院自动控制系,北京100081

出　　处：《模式识别与人工智能》2008年第2期155-159,共5页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金资助项目(No.60453001)

摘　　要：利用快速二维熵的图像分割方法因其运算量大而影响了它的实际使用。本文经过参数变换用减法代替原来方法中的对数运算.同时将PSO算法代替原来的穷尽搜索获得阈值向量来提高求解速度和分割效率,通过对实际图像分割效果表明,本文方法简单有效.The computation consuming of 2D maximum entropy method is often an obstacle in the image segmentation. In this paper logarithm is replaced by subtraction and the threshold vector is obtained by using a new optimization algorithm. The new algorithm is proposed to realize the 2D maximum entropy method instead of exhaustive search method, thus it is faster than the traditional method. The proposed method has been proved to be efficient through the example for segmenting the infrared image.

关 键 词：二维熵 粒子群优化(PS0) 图像分割 阈值 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]