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Corner detection is a fundamental step in image processing, and it takes an important role in target tracking, image stitching and three-dimension reconstruction. Harris algorithm is widely used in corner detection for simple calculation and its detection result is not affected by image rotation and light intensity changes. Harris algorithm uses integral differential mask to extract the image gradient, and the edges information remains in the low frequency part of images. When dealing with images with a large number of edge information, integral differential weakens the low frequency part of images obviously, thus the detection result is not really good. Besides, Harris algorithm does not have the property of scale-invariant. For these reasons, fractional differential and multiple scale-space method are put forward in this article to improve Harris algorithm. Experiments show that the detection result of improved algorithm is better than original Harris algorithm in dealing with images of much detailed information

Improvements Based on the Harris Algorithm