An Improved Corner Detection Algorithm Based on Gaussian Smoothing

摘要：

Corner point is the pixel with a high curvature on image edge. It is a key feature in digital image processing. Through the utilizing of corner points in image processing tasks, the computational complexity can be highly reduced. This paper proposes an improved corner detection algorithm. A technique using the radius of the fitting circle to denote local curve curvature is applied on the basis of image edge after Gaussian smoothing, and then a method using threshold is provided to decide the support region. Finally, mean k-cosine method is used to calculate the support angle and the false corners are picked out from the candidate corner set. Compared with classical algorithm, the experimental result indicates that the method in this paper is efficient and accurate when extracting corner feature from 2D images.

关键词： corner detection Gaussian smoothing support region mean k-cosine method

作者: WANG Chun SUN Guangmin WANG Yangye XU Lei

作者单位: Department of Electronic Engineering, Beijing University of Technology, Beijing, 100124, China

会议类型: 国际会议

会议名称: 2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

会议地点: 长沙

会议语种:英文

页码: 536-539

在线出版日期: 2010-05-11（万方平台首次上网日期，不代表论文的发表时间）