Edge detection using multi-directional anisotropic Gaussian directional derivative

Edge detection is a crucial task for computer vision. In this paper, we propose to use both the multi-directional first-order anisotropic Gaussian derivative and the second-order anisotropic Gaussian derivative to extract image gray information. The first-order derivative is utilized to determine the gradient direction, while the second-order derivative is used to identify the gradient magnitude. By double filtering of the feature information, the operator's robustness is improved, and the edge stretching is reduced. The multi-directional filters can obtain enough gradient information to avoid edge missing. Moreover, we propose to use the adaptive thresholds to improve the operator's generalizability. The aggregate receiver operating characteristic curve shows that the proposed method improves the accuracy of edge detection and exhibits strong robustness.