The more years a ceramic plate has endured, the more cracks will form on its surface, creating a complex network. The interwoven cracks combine to form particles of various sizes and shapes. The identification and analysis of these particles can effectively determine the age of the plate, its material properties, the manufacturing techniques of that time, and its artistic value. This article presents a newly studied image segmentation algorithm for multiple crack delineation in ancient dish patterns. It consists of a special image preprocessing sub-algorithm that uses a newly designed fractional differential template different from the ordinary template, and it can remove noise more effectively; an improved Canny edge detector based on the image preprocessing, in which the two thresholds can be auto-decided according to the image information; and a number of post-functions for forming particles by cracks, which mainly rely on region splitting and merging based on particle shape analysis. The studied algorithm has been tested on more than a hundred ancient dish images, and it has been compared to several existing widely used image segmentation algorithms and methods, such as edge-detection-based, gray-level similarity-based, and deep learning methods; the results show that the new algorithm produces fewer over-segmentation and under-segmentation problems, and it works satisfactorily.
Li et al. (Sun,) studied this question.