Edge detection using orthogonal moment-based operators

Presents a new approach to detect step edges with subpixel accuracy. The proposed approach is based on a set of orthogonal complex moments of the image known as Zernike moments. An ideal 2-D step edge is modeled in terms of four parameters: the background gray level, the step size, the distance of the edge from the center of the mask, and the orientation of the edge. Discrete Zernike moments are used to obtain a total of three masks to compute all the edge parameters for subpixel detection. For pixel-level edge detection only two masks (one real and one complex) are required. The theoretical analysis of the influence of noise on the location and the orientation of an edge is presented. This analysis reveals that the accuracy of the proposed approach is virtually unaffected by the additive noise. Experimental results are presented to demonstrate the efficacy of the proposed technique