[1]龙鹏,鲁华祥.方差不对称先验信息引导的全局阈值分割方法[J].智能系统学报编辑部,2015,10(5):663-668.[doi:10.11992/tis.201412022]
 LONG Peng,LU Huaxiang.Global threshold segmentation technique guided by prior knowledge with asymmetric variance[J].CAAI Transactions on Intelligent Systems,2015,10(5):663-668.[doi:10.11992/tis.201412022]
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方差不对称先验信息引导的全局阈值分割方法(/HTML)
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年5期
页码:
663-668
栏目:
出版日期:
2015-10-25

文章信息/Info

Title:
Global threshold segmentation technique guided by prior knowledge with asymmetric variance
作者:
龙鹏 鲁华祥
中国科学院 半导体研究所, 北京 100083
Author(s):
LONG Peng LU Huaxiang
Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
关键词:
Otsu方法图像分割方差差异全局阈值先验信息
Keywords:
Otsu methodimage segmentationvariance discrepancyglobal thresholdprior knowledge
分类号:
TP751
DOI:
10.11992/tis.201412022
文献标志码:
A
摘要:
图像分割是图像分析的关键步骤,其中阈值分割方法是最简单也是应用最广泛的方案。Otsu方法在应用于通用的现实图片时,由于其保持着良好的稳定性和分割目标的形状测度,被认为是最好的方法之一。但是大量研究表明对于2类方差差异很大的图像,其阈值严重偏离最优阈值,而偏向方差大的一类。研究了Otsu最优准则和现有改进算法的特性,进而基于前景与背景方差差异先验信息提出了新的最优化准则。与现存的非类间方差阈值法和对Otsu阈值法进行改进的方法进行比较表明,该方法具有最优的特性,同时不需要可变参数。
Abstract:
Image segmentation is a fundamental step in image processing, and threshold segmentation is the simplest and most widely used method among the segmentation methods. The classic Otsu method is deemed as one of the best methods for general real world images with regard to uniformity and shape measure. However, a lot of research shows that, for two classes of image with large variance difference, the threshold seriously deviates from the opti-mum threshold and inclines to the type with larger variance. In this paper, optimal Otsu criteria and the properties of an existing improved version are analyzed, then a novel criterion of optimization is proposed by combining prior knowledge about the variance discrepancy between background and foreground. The method is compared with the current non-between-class variance threshold methods and some improved Otsu threshold methods. The results show that our method is optimal, with no need for variable parameters.

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备注/Memo

备注/Memo:
收稿日期:2014-12-17;改回日期:。
基金项目:中国科学院战略性先导专项基金资助项目(xda06020700).
作者简介:龙鹏,男,1990年生,硕士研究生,主要研究方向为医学图像、彩色图像分割、图像检索以及模式分析;鲁华祥,男,研究员,博士生导师,博士,主要研究方向为半导体神经网络技术及其应用。曾获北京市科学技术进步一等奖、中国科学院盈科优秀青年学者奖、国家发明三等奖,国家“八五”科技攻关重大科技成果奖,95’电子十大科技成果奖。国际首创“半导体人工神经网络硬件及其软件”,“半导体工业生产优化问题的人工神经网络模型、算法与应用”,独创“高精度双权值突触神经元计算机CASSANN-Ⅱ”等重大科研成果。
通讯作者:龙鹏.E-mail:longpeng2008to2012@gmail.com.
更新日期/Last Update: 2015-11-16