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 a- chieved linear intercept histogram,the thresholding is performed according to the Arimoto entropy,and the ob- tained 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-pix- el-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 mo- ment-based edge extraction methods.The results show that the proposed method is superior in both speed and accu- racy.

Key words: cutting tool image, sub-pixel edge detection, linear intercept histogram, Arimoto entropy, Zernike mo- ment