首页 | 本学科首页   官方微博 | 高级检索  
     

基于三维指数灰度熵的快速图像分割算法
引用本文:张书真,黄光亚.基于三维指数灰度熵的快速图像分割算法[J].计算机工程与应用,2013,49(21):119-122.
作者姓名:张书真  黄光亚
作者单位:吉首大学 信息科学与工程学院,湖南 吉首 416000
基金项目:国家自然科学基金(No.61262032);湖南省教育厅科学研究项目(No.12C0314)。
摘    要:针对现有阈值分割法通常只考虑图像直方图的统计信息,而忽略了图像目标和背景类内灰度分布的均匀性,提出指数灰度熵分割算法,并推广得到三维指数灰度熵分割算法。给出了一维指数灰度熵阈值法及三维指数灰度熵阈值法的原理,在三维直方图上,将降维处理和优化搜索策略相结合,得到最优分割阈值。理论证明,阈值搜索复杂度由原来的O(L3)]降至O(L12)]。实验结果表明,与现有的多种阈值法相比,所提算法抗噪性能更强、分割效果更优,且运算时间大为减少。

关 键 词:阈值分割  三维直方图  指数灰度熵  降维处理  搜索策略  

Fast image segmentation algorithm based on three-dimensional exponential gray entropy
ZHANG Shuzhen , HUANG Guangya.Fast image segmentation algorithm based on three-dimensional exponential gray entropy[J].Computer Engineering and Applications,2013,49(21):119-122.
Authors:ZHANG Shuzhen  HUANG Guangya
Affiliation:School of Information Science and Engineering, Jishou University, Jishou, Hunan 416000, China
Abstract:In view of the existing threshold segmentation methods which usually only consider the statistical information from image histogram, while ignoring the gray distribution uniformity of the image target class and the background class, one-dimensional exponential gray entropy segmentation algorithm is put forward and three-dimensional exponential gray entropy segmentation algorithm is deduced. The principles of one-dimensional exponential gray entropy algorithm and three-dimensional exponential gray entropy algorithm are presented. The optimum segmentation threshold is got by combining dimension reduction and optimal search strategy on the three-dimensional histogram. The search complexity is reduced from O(L3) to O(L1/2) in theory. Experimental results show that, compared with other existing threshold algorithms, the proposed algorithm has better anti-noise performance, segmentation effect and its operation time is reduced greatly.
Keywords:threshold segmentation  three-dimensional histogram  exponential gray entropy  dimension reduction method  search strategy
本文献已被 维普 万方数据 等数据库收录!
点击此处可从《计算机工程与应用》浏览原始摘要信息
点击此处可从《计算机工程与应用》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号