The Comparison of Two Typical Corner Detection Algorithms

Corners in images represent a lot of important information. Extracting corners accurately is significant to image processing, which can reduce much of the calculations. In this paper, two widely used corner detection algorithms, SUSAN and Harris corner detection algorithms which are both based on intensity, were compared in stability, noise immunity and complexity quantificationally via stability factor eta, anti-noise factor rho and the runtime of each algorithm. It concluded that Harris corner detection algorithm was superior to SUSAN corner detection algorithm on the whole. And the comparison result was applied to an image matching experiment. It was verified that the quantitative evaluations of these two corner detection algorithms were valid through calculating match efficiency, defined as correct matching corner pairs dividing by matching time, which can reflect the performances of a corner detection algorithm comprehensively. The work of this paper can provide a direction to the improvement and the utilization of these two corner detection algorithms.