%0 Journal Article
%T Maximum entropy thresholding algorithm based on mean-median-gradient cooccurrence matrix model<br>基于均值—中值—梯度共生矩阵模型的最大熵分割算法*
%A LONG Jian-wu
%A SHEN Xuan-jing
%A WEI Wei
%A HE Yue
%A CHEN Hai-peng
%A <br>龙建武
%A 申铉京
%A 魏巍
%A 何月
%A 陈海鹏
%J 计算机应用研究
%D 2010
%I 
%X In order to overcome the shortcomings of maximum entropy thresholding algorithm based on gray level-gradient co-occurrence matrix model with poor antinoise performance, this paper introduced a mean-median-gradient co-occurrence matrix model. Based on this model, proposed a maximum entropy thresholding algorithm simultaneously. For the purpose of saving computing time and storage space, presented a fast recursive method in the end. Experimental results show that the algorithm is superior to gray level-gradient model segmentation approach, and can suppress Gaussian noise, impulse noise and their hybrid noise, improves the robustness of the segmentation effectively.
%K gray level-gradient co-occurrence matrix
%K mean-median-gradient co-occurrence matrix
%K maximum entropy
%K threshold
%K image segmentation<br>灰度—梯度共生矩阵
%K 均值—中值—梯度共生矩阵
%K 最大熵
%K 阈值
%K 图像分割
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=73B330A4E2A54F2B0755C47DA443E00E&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=9CF7A0430CBB2DFD&sid=21478D1C3F0F776A&eid=008945FD5E2305F5&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=1&reference_num=11