The method of repairing OHE damaged ancient painted murals based on machine vision

Abstract

Traditional mural repair methods only observe the texture of murals when segmenting the repair area, but ignore the extraction of a mural damage data, resulting in incomplete damage crack information. For this reason, the method of repairing the damaged murals based on machine vision is studied. Using machine vision, it can get two-dimensional image of a mural, preprocess the image, extract the damaged data of a mural, and then divide the repair area and repair degree index. According to different types of damage, it can choose the corresponding repair methods to achieve the repair of damaged mural. The results show: Compared with the OPTICS-based unsupervised method and the machine vision for orchard navigation method, the number of repair points and repair cracks extracted by the proposed method is more than that of the two traditional methods, which can more accurately and comprehensively extract the repair information of murals.