Title: An adaptive sub-pixel edge detection method based on improved Zernike moment

Authors: Jiadi Mo; He Yan; Jihong Liu

Addresses: The College of Liangjiang Artificial Intelligence, Chongqing University of Technology, Banan District, Chongqing, China ' The College of Liangjiang Artificial Intelligence, Chongqing University of Technology, Banan District, Chongqing, China ' The College of Liangjiang Artificial Intelligence, Chongqing University of Technology, Banan District, Chongqing, China

Abstract: Sub-pixel edge detection is one of the most basic procedures in the field of vision measurement as an important step for high-precision measurement. For the traditional Zernike moment-based sub-pixel edge detection algorithms, it is difficult to obtain a suitable greyscale threshold for different images, which greatly affects the accuracy of vision measurement. This paper proposes a new sub-pixel edge detection method based on improved Zernike moment, which is an adaptive, robust and effective method for high-precision measurement. The ideal step edge is modelled in three-grey-step edge model, and for the solution of edge parameters only two Zernike moments are required. According to the characteristics of greyscale in three-grey-step edge model, the greyscale of noise and edge can be clarified into two categories to obtain a suitable threshold according to k-means clustering. Experimental results show that the proposed method can obtain an appropriate greyscale threshold according to different images, and has good performance in locating edges.

Keywords: Zernike moment; sub-pixel edge detection; adaptive threshold; three-grey-step edge model.

DOI: 10.1504/IJWMC.2022.123314

International Journal of Wireless and Mobile Computing, 2022 Vol.22 No.2, pp.140 - 147

Received: 10 Oct 2021
Accepted: 29 Jan 2022
Published online: 08 Jun 2022 *

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