A Multiscale and Anisotropic Edge Detection Algorithm

Edge detection is of important significance for computer vision. Current edge detection methods based on the first derivative or second derivative such as Canny and Laplace and Gaussian operators use single scale information and do not take account of the edge directions sufficiently. Those operators cannot distinguish edges and noise well. A multiscale and multidirectional edge detection algorithm is proposed for gray images in this paper. Gaussian function is used as the filter kernel. A serials scales and directional filters are generated by the generating function. A bank of edge maps are acquired by convoluting the original image with those filters. The maximum response is used to find the local maximum in the maps. Finally, the edges are determined after a certain threshold. The experiments show that the proposed algorithm gets outperformance compare to some state of art methods.