Orthogonal moment operators for subpixel edge detection

• Published in: Pattern recognition Vol. 26; no. 2; pp. 295 - 306
• Main Authors: Ghosal, Sugata, Mehrotra, Rajiv
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 1993; Elsevier Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Edge detection; Exact sciences and technology; Feature detection; Image segmentation; Orthogonal moments; Pattern recognition. Digital image processing. Computational geometry; Subpixel edge detection; Zernike moments; Zernike moments; Image segmentation; Subpixel edge detection; Feature detection; Edge detection; Orthogonal moments; Segmentation; Image processing; Zernike moment; Orthogonal moment
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/0031-3203(93)90038-X

A new approach to detect step edges with subpixel accuracy is presented. The proposed approach is based on a set of orthogonal complex moments of the image known as Zernike moments. An ideal two-dimensional (2D) 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 complex 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. The technique is effective in detecting both the pixel-level and subpixel-level edges. Experimental results are presented to demonstrate the efficacy of the proposed technique.