二维直方图斜分最大类间交叉熵的图像分割  

Image segmentation of maximum inter-class cross entropy based on 2D histogram oblique segmentation

作　　者：张新明[1] 刘斌[2] 李双[1] 张慧云[1] 

机构地区：[1]河南师范大学计算机与信息技术学院,河南新乡453007 [2]湖北大学数学与计算机科学学院,武汉430062

出　　处：《计算机应用》2010年第9期2453-2457,共5页journal of Computer Applications

基　　金：湖北省自然科学基金重点项目(2009CDA133);河南省重点科技攻关项目(092102210017;102102210180);河南省教育厅科技攻关项目(2008B520021)

摘　　要：利用二维直方图斜分原理,提出了一种基于最大类间交叉熵的快速图像分割方法。首先依据二维直方图斜分法构建最大类间交叉熵阈值选取公式,然后导出这种最大类间交叉熵阈值选取的快速递推算法,最后将定义的数组运算与这种快速算法相结合搜索最佳阈值向量,使整个算法更简明高效。实验结果表明,与当前二维直方图斜分阈值方法相比,此算法效率更高,通用性更强。With the approach based on 2D histogram oblique segmentation, a fast image thresholding method based on maximum inter-class entropy was presented in this paper. Firstly the thresholding method was formulated, based on maximum inter-class entropy and 2D histogram oblique segmentation, and then its fast recurring algorithm was deduced, finally the defined array operations and the algorithm were combined to search the optimal threshold vector. The experimental results show that the proposed method can not only get better generalization, but also has higher efficiency than the current thrsholding method based on 2D oblique segmentation.

关 键 词：图像分割 阈值化 二维直方图斜分 最大类间交叉熵 递推算法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TN911.73[自动化与计算机技术—计算机科学与技术]