The Harris Corner Detection Method Based on Three Scale Invariance Spaces

In order to solve the problem that the traditional Harris comer operator hasn’t the property of variable scales and is sensitive to noises, an improved three scale Harris corner detection algorithm was proposed. First, three scale spaces with the characteristic of scale invariance were constructed using discrete Gaussian convolution. Then, Harris scale invariant detector was used to extract comers in each scale image. Finally, supportable and unsupportable set of points were classified according to whether the corresponding corners in every scale image support that of the original images. After the operations to those unsupportable set of points, the noised corners and most of unstable corners could be got rid of. The corners extracted by the three and the original scale spaces also had scale invariant property. The experiments results proved that, compared with the scale space on the whole Gaussian pyramid, the utilization factor of the image was increased, the calculation time is decreased, and the image was high recurrence rate and stability.