An improved binarization algorithm of wood image defect segmentation based on non-uniform background

In this study, an image binarization optimization algorithm, based on local threshold algorithms, is proposed because global and traditional local threshold segmentation algorithms cannot effectively address the problems of non-uniform backgrounds of wood defect images. The proposed algorithm calculates the threshold by the mean, standard deviation and the extreme value of the window. The results indicate that this modified algorithm enhances the image segmentation for wood defect images on a complex background, which is much superior to the global threshold algorithm and the Bernsen algorithm, and slightly better than the Niblack algorithm and Sauvola algorithm. Compared with similar models, the algorithm proposed in this paper has higher segmentation accuracy, as high as 92.6% for wood defect images with a complex background.