期刊文献+

基于阈值的图像分割算法研究综述:原理、分类及典型算法 被引量:10

A review of threshold-based image segmentation algorithms:Principles,classification and typical algorithms
在线阅读 下载PDF
导出
摘要 随着计算机技术的飞速发展,图像处理技术在各个领域都得到了广泛应用,如产品质量检测、医学图像处理、军事目标的定位与跟踪等。作为图像处理技术和计算机视觉技术的研究基础,图像分割技术目前已出现了大量不同类型的算法,并在各个领域的应用中发挥着重要的作用。其中,基于阈值的图像分割算法因具有简单有效、计算量小、性能稳定等优点而受到了人们的普遍青睐。首先,对图像分割技术按照不同的划分方式进行了简单的分类;其次,对阈值分割算法的基本原理、分类及最典型的Otsu算法的基本思想进行了详尽的介绍;最后,对阈值分割算法目前存在的问题进行了阐述,并对算法未来的发展趋势进行了展望。研究工作可为图像处理技术的进一步发展提供理论借鉴。 With the rapid development of computer technology,image processing technology has been widely used in various fields,such as product quality detection,medical image processing,military target positioning and tracking.As the basis of image processing technology and computer vision technology,a large number of different types of algorithms has emerged,and these algorithms play an important role in various fields of application.Among them,threshold based image segmentation algorithm has been welcomed because of its advantages of simple,effective,little computation and stable performance.Firstly,the image segmentation technology is simply classified according to the different partitioning ways.Secondly,the basic principle,classification,and the basic idea of the most typical Otsu algorithm of threshold segmentation algorithm are introduced in detail.At last,the existing problems of threshold segmentation algorithm are described,and the future development trend of this algorithm are forecasted.This work can provide theoretical reference for the further development of image processing technology.
作者 杨林蛟 YANG Linjiao(College of Chemistry and Chemical Engineering,Shenyang Normal University,Shenyang 110034,China)
出处 《沈阳师范大学学报(自然科学版)》 CAS 2023年第6期526-529,共4页 Journal of Shenyang Normal University:Natural Science Edition
基金 辽宁省教育厅科学研究经费项目(LJC202004,LJC202005)。
关键词 图像处理 阈值分割 阈值选取 算法 image processing threshold segmentation threshold selection algorithm
作者简介 杨林蛟(1976-),男,青海西宁人,沈阳师范大学高级实验师,硕士。
  • 相关文献

参考文献3

二级参考文献22

  • 1Pavlids T. Why progress in machine vision is so slow [ J ]. Pattern Recognition Letters, 1991,13(4) :221 - 225.
  • 2Sahoo P K,Soltani S, Wang A K C.A survey of thresholding techniques[J].Computer Vision, Graphics and Image Processing, 1988,41 (2) :233 - 260.
  • 3Pong T C, Shapiro L G, Watson L T. Experiments in segmentation using face model region grower [J]. Computer Vision, Graphics and Image Processing, 1984,25(1) :1-23.
  • 4Monga O. An optimal region growing algorithm for image segmentation[J]. Inte.J Pattern Recog. Artif. Intell, 1987,1(4) :351 - 375.
  • 5Giordana .N, Pieczynski W. Estimation of generalized multisensor hidden markov chains and unsupervised image segmentation [ J]. IEEE Trans on PAMI, 1997,19(5) :465 - 475.
  • 6Tabb M, Ahuja M. Multiscale image segmentation by integrated edge and region detection [J] .IEEE. Trans on IP, 1997,6(5) :642 -654.
  • 7Wong A K C, Sahoo P K. A gray-level threshold selection method based on maximum entropy principle [J]. IEEE. Trans.on SMC, 1989,19(4) :866 - 871.
  • 8Chanda B, Majumder D, Dutta R. A note on the use of gray-level co-occurrence matrix in threshold selection [ J]. Signal Processing, 1988,15 :149- 167.
  • 9Kawaguchi E, Taniguchi R I. The depth first picture expression as an image thresholding strategy[J]. IEEE. Trans on SMC, 1989, 19(5):1321 - 1328.
  • 10Pal S K, King R A, Hashim A A. Automatic gray-level thresholding through index of fuzziness and entropy [ J ]. Pattern Recognition Letters, 1983,1 : 141 - 146.

共引文献445

同被引文献102

引证文献10

二级引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部