Research on a Bone Stick Fragment Stitching Method Based on Improved Corner Detection

The stitching of bone stick fragments is of great significance for the inheritance of and research into the outstanding traditional culture of the Western Han Dynasty. Focused on the problem that the existing methods have a low bone stick fragment stitching success rate due to their complex edges, this paper proposes a bone stick fragment stitching method based on improved corner detection, which strengthens the features of broken edges and improves the success rate of stitching. First, the maximum outer contour of the bone stick fragment is obtained using connectivity analysis. Then, the broken edge features are enhanced by combining multi-scale and multi-stage corner detection, as well as a double screening mechanism of local template and corner density analysis. On this basis, the maximum outer contour is segmented using a region segmentation method based on corner features. Then, a priority matching strategy is used to perform multi-dimensional sequential priority matching of corner features for the densest segments and to match other contour segments in the order of density. Finally, based on the matching results, the bone stick fragment is stitched exactly and restored. Experimental results show that compared with other algorithms, the average proportion of broken corners when using the method in this paper is increased by about 44.89%, and the average comprehensive weighted similarity is increased by about 32.42%, which meets the needs of bone stick fragment stitching.