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

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. Copyright © 2022 Inderscience Enterprises Ltd.