计算机科学 ›› 2013, Vol. 40 ›› Issue (3): 295-298.

• 图形图像与模式识别 • 上一篇    下一篇

一种结合粒子群算法和自适应加权窗的二维Otsu图像分割新方法

颜学颖,焦李成   

  1. (西安电子科技大学智能感知与图像理解教育部重点实验室 西安710071)
  • 出版日期:2018-11-16 发布日期:2018-11-16

New 2D Otsu Image Segmentation Method via Particle Swarm Algorithm and Adaptive Weighted Window

  • Online:2018-11-16 Published:2018-11-16

摘要: 针对传统二维Otsu门限分割方法中滤噪和小目标保持性能的不足,提出了一种基于自适应加权窗的二维Otsu门限分割的新方法。新方法对二维Otsu的部域窗口设置方法做了改进,使用中心点的局部平稳特征来自适应地确定下一邻域窗口的尺寸大小,然后利用粒子群算法来加快门限的计算速度,从而提高门限分割的性能。实验结果表明:与目前广泛使用的一维Otsu、二维Otsu方法以及直线型门限二维Otsu方法相比,新方法有着更好的门限分割效果,并且有更好的噪声抑制和目标保持效果。

关键词: 二维Otsu,自适应加权窗,粒子群算法,图像门限分割

Abstract: Aimed at the shortage of the abilities of noise removing and small target preservation for the conventional two-dimensional Otsu thresholding method, a new two-dimensional (2D) Otsu method based on adaptive weighted window was proposed. hhe new method improves the window setting method of the 2D Otsu, and the window size is adaplively determined by the local stationarity character. Then, the threshold is computed by the particle swarm algorithm, in order to improve the segmentation performance and shorten the computational time. Compared with the commonly-used oncdimensional Otsu, 2D Otsu method and linctype threshold 2D Otsu method, the proposed method has the better segmentation performance, with better performance for noise removal and small target preservation.

Key words: Two-dimensional Otsu, Adaptive weighted window, Particle swarm algorithm, Image thresholding segmentation

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!