Sub-Pixel Edge Detection of Cutting Tool Images Based on Arimoto Entropy and Zernike Moment

Affiliation of Author(s):电子信息工程学院

Journal:Huanan Ligong Daxue Xuebao

Abstract:In order to meet the high-speed and high-accuracy demands of the machine vision-based measurement system of cutting tool sizes, an image sub-pixel edge detection method based on Arimoto entropy of linear intercept histograms and Zernike moment is proposed. In the method, first, the neighborhood-average grayscale of images is obtained through the Gaussian sliding window to construct a two-dimensional histogram, and the linear intercept method is adopted to reduce the two-dimensional histogram to a one-dimensional histogram. Then, aiming at the achieved linear intercept histogram, the thresholding is performed according to the Arimoto entropy, and the obtained threshold is mapped back to the two-dimensional histogram to extract a target region and pixel-level edges. Finally, the edge points are re-located by using the Zernike moment-based edge model, thus achieving the sub-pixel-level edges of cutting tool images. By a large number of experiments on the cutting tool images, the proposed method is compared with the Canny-based, the space moment-based, the gray moment-based and the Zernike moment-based edge extraction methods. The results show that the proposed method is superior in both speed and accuracy. © 2017, Editorial Department, Journal of South China University of Technology. All right reserved.

ISSN No.:1000-565X

Translation or Not:no

Date of Publication:2017-12-01

Co-author:龙云淋,zy

Correspondence Author:wyq

Date of Publication:2017-12-01