计算机科学 ›› 2015, Vol. 42 ›› Issue (Z6): 171-174.

• 模式识别与图像处理 • 上一篇    下一篇

基于图像复杂度的一维Otsu改进算法

董忠言,蒋理兴,王俊亚,肖凯   

  1. 解放军信息工程大学 郑州450000,解放军信息工程大学 郑州450000,解放军信息工程大学 郑州450000,解放军信息工程大学 郑州450000
  • 出版日期:2018-11-14 发布日期:2018-11-14

Modified One-dimensional Otsu Algorithm Based on Image Complexity

DONG Zhong-yan, JIANG Li-xing, WANG Jun-ya and XIAO Kai   

  • Online:2018-11-14 Published:2018-11-14

摘要: 自适应二值化技术广泛应用于图像分割和目标边缘的提取,其阈值的确定是数字图像处理的关键技术。经典Otsu算法是穷举式的阈值确定方法,存在较大的计算冗余。在内存和资源都十分有限的条件下,提出了一种基于图像复杂度的一维Otsu改进算法,根据图像复杂程度的不同,该算法在满足准确度要求的基础上提高了Otsu算法的速度。在DM3730实验板上进行了实验,结果表明,该算法的复杂度低于经典算法,计算速度可提升40%左右,可以满足嵌入式系统的实时性要求,且分割效果与原始算法基本一致。

Abstract: Self-adaptive binaryzation is widely used in image segmentation and edge detection.Threshold extraction is one of the key technologies of digital image processing.Classic Otsu algorithm is an exhaustivity way and there will be a large computing redundancy.On condition of limited computer RAM and resource,this paper put forward a modified one-dimensional Otsu algorithm based on image complexity.According to different complexity,it improves Otsu algorithm speed on the basis of accuracy requirements.According to the experiments done on DM3730,this algorithm’s complexity is better than the classic one and its speed can be improved by about 40%.It can meet embedded system’s real-time requirements and the segmentation effect is almost the same with the classic one.

Key words: Otsu algorithm,Image complexity,Average gray value,Self-adaptive segmentation

[1] Sonka M,Hlavac V,Boyle R.Image processing:Analysis andMachine Vision[D].Beijing:Posts & Telecom Press,2003
[2] Sahoo P K.A survey of threshold techniques[J].Computer Vision Graphic,Image Process,1988,41(2):233-260
[3] 高永英.一种基于灰度期望值的图像二值化算法[J].中国图形图像学学报(A版),1999,4(6):524-528
[4] Blayvas I,Bruckstein A,Kimmel R.Efficient Computation of Adaptive Threshold Surfaces for Image Binarization[C]∥Proceedings of 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognation.2011,1:737-742
[5] 王积分,张新荣.计算机图像识别[M].北京:中国铁道出版社,1998:75-77
[6] Otsu N.A threshold selection method from gray level histo-grams[J].IEEE Transactions on System,Man,and Cybernetic,1979,9(1):62-66
[7] Ridler T W,Calvard S.Picture thresholding using an iterativeselection method[J].IEEE Transactions on System,Man,and Cybernetic,1978,8:630-632
[8] PunT.Entropic Thresholding:A New Approach[J].Computer Vision Graphics Image Process,1981,6(3):210-239
[9] Niblack W.An Introduction to Digital Image Processing[M].Prentice Hall,1986:115-116
[10] Bernsen J.Dynamic thresholding of gray-level images[C]∥Eig-hth International Conference on Pattern Recognition.France:IEEE Computer Society Press,1986:1251-1255
[11] Peters II Richard Alan,Strickland Robin N.Image complexity metrics for automatic target recognizers [C]∥Proceedings of Automatic Target Recognition System and Technology Conference,Naval Surface Warfare Center.Silver Spring,MD,USA,1990:1217
[12] 钟雪君.一种改进的Otsu双阈值二值化图像分割方法[J].电子世界,2013,4(2):104-105
[13] 龙钧宇,金连文.一种基于全局均值和局部方差的图像二值化方法[J].计算机工程,2004,30(2):70-72
[14] 王勇智.数字图像的二值化处理技术探究[J].湖南理工学院学报:自然科学版,2005,18(1):31-33
[15] 朱齐丹,荆丽秋,毕荣生,等.最小误差阈值分割法的改进算法[J].光电工程,2010,37(7):107-113
[16] 高振宇,杨晓梅,龚剑明,等.图像复杂度描述方法研究[J].中国图像图形学报,2010,15(1):129-135

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!