[1]曲昆鹏,郑丽颖.基于目标、背景比例的灰度图像自动阈值选取法[J].应用科技,2010,(02):52-54.[doi:10.3969/j.issn.1009-671X.2010.02.013]
 QU Kun-peng,ZHENG Li-ying.Automatic thresholding of gray scale image based on the proportion of object and background[J].yykj,2010,(02):52-54.[doi:10.3969/j.issn.1009-671X.2010.02.013]
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基于目标、背景比例的灰度图像自动阈值选取法(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
期数:
2010年02期
页码:
52-54
栏目:
计算机技术与应用
出版日期:
2010-02-05

文章信息/Info

Title:
Automatic thresholding of gray scale image based on the proportion of object and background
文章编号:
009-671X (2010) 02-0052-03
作者:
曲昆鹏郑丽颖
哈尔滨工程大学 计算机科学与技术学院,黑龙江 哈尔滨,150001
Author(s):
QU Kun- pengZHENG Li-ying
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
关键词:
Otsu法图像分割阈值化方差
Keywords:
Otsu algorithm image segmentation thresholding variance
分类号:
TP391.41
DOI:
10.3969/j.issn.1009-671X.2010.02.013
文献标志码:
A
摘要:
最大类间方差法(Otsu法)因其计算简单、自适应性强而成为被广泛使用的图像阈值自动选取方法.在分析Otsu法原理的基础之上,提出了一种改进的最大类间方差法.为了提高分割效果,该方法同时考虑了背景和目标的类间距离和类内距离.与同类方法相比,提出的方法将目标和背景所占的比例作为权值修正了现有的方法,使得衡量类内距离的目标与背景的平均方差按照目标与背景的面积划分.Lena、 Cameraman标准测试图像以及杂草图像的仿真结果验证了本方法的有效性.
Abstract:
Otsu algorithm is widely used in image segmentation thanks to its simplicity and selfadaptation. After studying Otsu thresholding algorithm, an improved method is developed in this paper. By combining the between class distance and within class distance of object and background, a better segmentation is achieved with the new proposed thresholding method. The method proposed in this paper outperforms the present similar methods by using the proportion of object and background as weight values, to make the average variance of the object and the background segmented by their respective areas. The simulation results of standard testing images as well as weed images show the effectiveness of the proposed method.

参考文献/References:

[1] GONZALEZ R C, WOODS R E. Digital image processing[M]. 2nd ed. Beijing: Publishing House of Electronics Industry, 2001. \
[2]LEE S, CHUNG S, PARK R H. A comparative performance study of several global thresholding techniques for segmentation[J]. Computer Vision, Graphics, and Image Processing, 1990 (52): 171-190.
[3]SAHOO P K, SOLTANI S, WONG A K C. A survey of thresholding technique[J] .Computer Vision graphics Image Process, 1988 (41): 233-260.
[4]OTSU N. A threshold selection method from graylevel histogram[J]. IEEE Trans on SMC9, 1979:62-66.
[5]PUN T. A new method for graylevel picture thresholding using the entropy of the histogram[J]. Signal Processing, 1980(2): 223-237.
[6]付忠良. 图像阈值选取方法Otsu方法的推广[J]. 计算机应用, 2000, 20(5):37-39. [7]蔡艳梅,吴庆宪,姜长生. 改进的Otsu的图像分割[J]. 光电与控制, 2007,14(6): 118-119.

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备注/Memo

备注/Memo:
作者者简介:曲昆鹏(1983-),男,硕士研究生,主要研究方向:计算机视觉与听觉,E-mail:kqp6@163.com.
更新日期/Last Update: 2010-03-16