加权调和平均型最大熵图像阈值选取法  

Image Threshold Selection Method Using Weighted Harmonic Average Maximum Entropy

作　　者：雷博[1] 范九伦[2] 

机构地区：[1]西安电子科技大学电子工程学院,西安710071 [2]西安邮电学院信息与控制系,西安710061

出　　处：《模式识别与人工智能》2009年第6期884-890,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60572133);陕西省教育厅专项科研计划项目(No.09JK721)资助

摘　　要：基于最大熵阈值法可看成是目标和背景信息熵的算术平均,基于此给出一种基于目标和背景信息熵的调和平均的图像阈值分割方法.为了进一步增强该方法的分割性能,又给出加权调和平均型最大熵图像分割法以及权重参数的一种选取方法.加权调和平均型最大熵图像分割法通过计算图像中目标和背景的加权调和平均熵,寻找加权熵最大的灰度值作为图像的分割阈值.实验表明该方法可获得比传统熵阈值法更好的分割效果.A harmonic average Shannon entropy threshold method is proposed based on the thought that the maximum entropy threshold method searches the optimal threshold of the image by maximizing the arithmetical average Shannon entropy of the object and the background. To improve the performance of the proposed method, a weighted harmonic average threshold method and a weight preferences method are proposed. The proposed weighted harmonic average threshold method searches the optimal threshold by maximizing the weighted harmonic entropy of the object and the background in an image. Experimental results show that the proposed method gets better segmentation result than the classical Shannon entropy threshold method.

关 键 词：阈值分割 最大熵 调和平均 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]