Local Color Space Correction for Color Difference in Machine Vision Imaging Based on Polynomial Regression

For correcting the color difference in machine vision imaging, all colors in the color space are usually used to train the correction model in traditional methods, which will lead to a complex training process of the model, long training time, and can not achieve high correction precision for each color. In order to solve these problems, a local color space correction scheme is proposed in this paper. In the scheme, the total color space is divided into smaller sub-color space based on color difference threshold and space distance respectively, and the colors in the sub-color space is corrected by polynomial regression method. The experimental results show that local color space correction has higher accuracy and more stable performance than total color space correction.