• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2013, Vol. 35 ›› Issue (6): 129-133.

• 论文 • 上一篇    下一篇

基于类内最小模糊散度的边缘扫描算法

唐兴艳,崔卫花   

  1. (重庆邮电大学经济管理学院,重庆 400065)
  • 收稿日期:2012-11-12 修回日期:2013-03-12 出版日期:2013-06-25 发布日期:2013-06-25
  • 基金资助:

    重庆市2011年度社会科学规划项目(2011QNGL65);重庆市研究生教育教学改革研究项目(yjg123054)

Edge detection algorithm based on
the smallest fuzzy divergence within a class      

TANG Xingyan,CUI Weihua   

  1. (College of Economics and Management,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
  • Received:2012-11-12 Revised:2013-03-12 Online:2013-06-25 Published:2013-06-25

摘要:

边缘检测在图像研究领域是一个重要的研究方向。针对边缘检测算法Canny算法中阈值需要人为干预设置等缺点,首先利用边缘保持滤波器替代原有的高斯滤波器,避免了对原图像的过度平滑现象,且有效地抑制了噪声;其次利用基于类内最小模糊散度的概念确定高低阈值,不需要人为干预,具有一定的自适应性。实验表明本算法在抑制噪声和边缘连续等方面具有良好的性能。

关键词: 边缘保持滤波器, 类内最小模糊散度, 边缘检测

Abstract:

Edge detection is a very important research aspect in image field. For the shortcomings the traditional Canny algorithm that thresholds requires human intervention, firstly, the paper replaced the original Gaussian filter with Edgepreserving filter, avoiding the phenomenon of oversmoothing for the original image and effectively suppressing the noise. Secondly, based on the smallest fuzzy divergence within a class, high and low thresholds were determined without requiring human intervention, so that the method is selfadaptive to some extent. Experiments show that the method has good performance in noise suppression and edge continuous, etc.

Key words: edge-preserving filter;the smallest fuzzy divergence within a class;edge detection