Wavelet transformation for gray-level corner detection

Abstract Corners are very attractive features for many applications in computer vision. In this paper, a new gray-level corner detection algorithm based on the wavelet transform is presented. The wavelet transform is used because the evolution across scales of its magnitudes and orientations can be used to characterize localized signals like edges and corners. Most conventional corner detectors detect corners based on the edge detection information. However, these edge detectors perform poorly at corners, adversely affecting their overall performance. To overcome this drawback, we first propose a new edge detector based on the ratio of the inter-scale wavelet transform modulus. This edge detector can correctly detect edges at the corner positions, making accurate corner detection possible. To reduce the number of points required to be processed, we apply the non-minima suppression scheme to the edge image and extract the minima image. Based on the orientation variance, these non-corner edge points are eliminated. In order to locate the corner points, we propose a new corner indicator based on the scale invariant property of the corner orientations. By examining the corner indicator the corner points can be located accurately, as shown by experiments with our algorithm. In addition, since wavelet transform possesses the smoothing effect inherently, our algorithm is insensitive to noise contamination as well.