Improvement of vision measurement accuracy using Zernike moment based edge location error compensation model

Abstract

This paper presents the Zernike moment based model developed to compensate edge location errors for further improvement of the vision measurement accuracy by compensating the slight changes resulting from sampling and establishing mathematic expressions for subpixel location of theoretical and actual edges which are either vertical to or at an angle with X-axis. Experimental results show that the proposed model can be used to achieve a vision measurement accuracy of up to 0.08 pixel while the measurement uncertainty is less than 0.36μm. It is therefore concluded that as a model which can be used to achieve a significant improvement of vision measurement accuracy, the proposed model is especially suitable for edge location of images with low contrast.