基于最大熵的目标分割和检测  

Image Segmentation and Detecting Based on Maximum Entropy

作　　者：韩涛[1] 辛欣[1] HAN Tao;XIN Xin(Chinese Flight Test Establishment,Xi'an 710089)

机构地区：[1]中国飞行试验研究院,西安710089

出　　处：《计算机与数字工程》2019年第4期820-822,共3页Computer & Digital Engineering

基　　金：国家自然科学基金"复杂地面背景下低可探测运动目标红外感知技术"(编号:F050105);航空基金"单光电经纬仪六维外部参数测量方法研究"(编号:2015ZD30002)资助

摘　　要：论文研究图像分割包含两个简单子图象的合成图象,这两个简单子图象的先验知识是它们拥有全局最大熵。图象概率密度函数表明是准高斯型形式。估计概率密度函数的参数,然后将最大似然比检验法用于分割。采用迭代算法提高分割的准确性,扩展该方法用于任意概率密度函数的图象分割。Segmentation of a composite image which contains two simple subimages is described.The a-priori knowledge about the two simple subimages is that they possess the maximum amount of entropy.The probability density functions(pdf s)of these image pixels are shown to be of the Quasi-gaussian form.Parameters for the pdf are estimated and then the maximum likelihood ratio test is applied to segmentation.An iterative algorithm is employed to improve the segmentation accuracy.Extension of this method to the segmentation of images with arbitrary pdf s is discussed.

关 键 词：图像分割 最大熵 概率密度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]