二维直方图准分的Renyi熵快速图像阈值分割  

Fast and Precise Two-Dimensional Renyi Entropy Image Thresholding

作　　者：张新明[1] 薛占熬[1] 郑延斌[1] 

机构地区：[1]河南师范大学计算机与信息技术学院,新乡453007

出　　处：《模式识别与人工智能》2012年第3期411-418,共8页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60873104);河南省重点科技攻关项目(No.092102210017;102102210554)资助

摘　　要：针对传统二维Renyi熵(RE)分割法分割结果不够准确和计算复杂度高的问题,提出一种快速的二维RE准分法.首先,用与主对角线平行的四条斜线将直方图分成内点区、边界点区和噪声点区,并对噪声点区进行去噪处理以便获得更好的分割性能.然后,对内点区与边界点区在RE公式中的对应量准确取值使阈值选取更准确.最后,提出二维RE准分法的一般递推算法,并在此算法的基础上利用RE在二维直方图上的计算特性和两个公式导出快速的二维RE阈值选取算法来降低计算复杂度.实验结果表明,与对比方法相比,文中方法不仅分割更准确和抗噪性更强,而且其运行时间少,与二维RE斜分法运行时间相近.In view of the inaccurate segmentation and the high computational complexity of the traditional two- dimensional （2-D） Renyi entropy （RE） thresholding method, a fast and precise 2-D RE image thresholding method is presented. Firstly, the 2-D histogram is divided into inner, edge and noise areas by four oblique lines in parallel with the main diagonal line, and the noise points of the noise areas in the 2-D histogram are eliminated to obtain better segmentation performance. Then, the values of inner and edge areas in the 2-D RE formula are calculated precisely to get a more accurate threshold. Finally, a recursive algorithm of the precise 2-D RE image thresholding method is proposed, and an approach based on the recursive algorithm is inferred with the computational features and two formulas of 2-D RE to reduce the computational complexity. The experimental results show that the proposed method achieves more accurate segmentation results and more robust anti-noise capability compared with other contrast methods, and its running time is much less, almost the same as that of the current RE recursive algorithm based on 2-D histogram oblique segmentation.

关 键 词：图像分割 阈值法 二维RENYI熵 递推算法 准分法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]