Multiscale Corner Detection of Gray Level Images Based on Log-Gabor Wavelet Transform

This paper presents two novel corner detection methods for gray level images based on log-Gabor wavelet transform. The input image is decomposed at multiscales and along multi-orientations. In the first algorithm, the magnitude along the direction that is orthogonal to the gradient orientation represents the "cornerness" measurement. Using this detector, corners are detected and localized accurately. Meanwhile, this method provides us the magnitudes and orientations along the principal axes corresponding to the directions of the local eigenvectors of the local changes. This information is useful when we deal with the affine transform applications in matching problems. The second proposed method is based on log-Gabor wavelets and second moment matrix. The input image is decomposed by the log-Gabor wavelets at multiscales along multi-orientations. Then the components at different scales and orientations are projected onto the x and y axis and formulated into the second moment matrix. Finally, the smaller eigenvalue of the second moment matrix is used to detect corner points. Compared with the most famous Harris detector and the recently published Kovesi's detector, the proposed methods provide better performance while retaining the good characteristics, such as robustness to noise, isotropic responses etc. Furthermore, the proposed methods address three problems existing in Harris detector. First, Harris method is based on single scale information. The proposed methods are based on the multiscales instead of single scale. Second, the derealization problem existing in Harris detector is minimized by utilizing good localization property of log-Gabor wavelets. Third, Harris detector has certain problems to detect corners of higher order structures. Due to the multi-orientation decomposition of the proposed methods, higher order structures are well computed. Consequently, the proposed methods detect corners of higher order structures very well. The proposed methods are first compared with Harris method and Kovesi's method in a subjective way. Then a classical stereo matching system is adopted to evaluate the corner detectors in an objective way. Both of the evaluations demonstrate better performance of the proposed methods.