基于二维Arimoto熵的阈值分割方法  

Image Thresholding Based on Two-Dimensional Arimoto Entropy

作　　者：卓问[1] 曹治国[1] 肖阳[1] 

机构地区：[1]华中科技大学图像识别与人工智能研究所多谱信息处理技术重点实验室,武汉430074

出　　处：《模式识别与人工智能》2009年第2期208-213,共6页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金重点项目(No.60736010);国家基础科研项目(No.A1420061266)资助

摘　　要：提出基于二维Arimoto熵的阈值分割方法.首先由图像的像素值及其邻域像素均值得到图像的二维直方图,然后从二维直方图中计算出二维Arimoto熵.当二维Arimoto熵达到最大时,对应的灰度级对即为分割阈值.通过引入二维联合幂概率分布建立快速算法,使算法速度大大提高,易于硬件实现.大量的对比实验表明,本文算法表现稳定,总体的分割效果优于基于二维Renyi熵和二维Shannon熵的阈值分割算法.A thresholding technique is proposed based on two-dimensional Arimoto entropy. Firstly, a two-dimensional histogram is determined by the gray value and the local average gray value of the pixels. Then, the two-dimensional Arimoto entropy is obtained from the two-dimensional histogram. The pair of gray values which makes the two-dimensional Arimoto entropy largest is the thresholding. By introducing in a two-dimensional joint power-probability distribution, a fast algorithm is proposed. The fast algorithm speeds up the implementation and makes the method suitable to real-time systems. Experiments indicate that the thresholding method based on two-dimensional Arimoto entropy gives a steady performance and it is better than the methods based on Renyi entropy and Shannon entropy.

关 键 词：图像分割 阈值 Arimoto熵 二维直方图 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]