An Improved Corner Detection Algorithm Based on Gaussian Smoothing

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.