Auto-adaptive harris corner detection algorithm based on block processing
Shizhe Shen, Xiaolong Zhang, Wei Heng · 2010
To eliminate the problems of extracting false corners and losing information of real corners and overcome the difficulty in finding a universal threshold in the non-maximal inhibition for the processing of all pictures in the Harris corner detection algorithm, an auto-adaptive threshold is introduced in this paper in order to generate more accurate corners. In addition, a method of block processing to divide an image into several blocks and process each block independently is proposed to ensure that the corners detected are evenly distributed in the image without clustering and thus eliminate the possibility that some corners may be lost because of the sharp contrast in gray scale in different parts of the image. Experimental results showed that this improved algorithm outperformed traditional and previous methods both in accuracy and evenness of distribution of detected corners.