Corner detection of gray level images using Gabor wavelets

This paper proposes a novel method for corner detection of gray level images using Gabor wavelets. Wavelet transform is a tool that can provide multiscale analysis while analyzing the local behavior of a signal. Gabor wavelets are known for their good localization in the time-frequency plane. Furthermore, they provide the shape and orientation information of local structures directly. In the proposed algorithm, the input image is decomposed at several wavelet scales and along several directions. The magnitude along the direction that is orthogonal to the gradient orientation represents the "cornerness" measurement. The proposed method is efficient since it has good localization, is robust to noise and achieves a high rate of true detection while keeping a low rate of false detection. Simulation results compare the proposed method with the two existing best approaches and show the good performance of the proposed method.