Damaged edges of bone tag images contain external factors such as impurities and damage, which affect the stitching process and lead to repair errors. Therefore, this paper proposes a stitching method based on image edge position feature matching. The objective is to improve the accuracy of image stitching by matching feature points based on the position of the image edge pixel so as to solve the accurate stitching of broken edge contour. In the first step of this method, the image containing the broken edge is preprocessed by edge detection, and the location of the broken edge pixel is proposed. Secondly, the feature descriptors were calculated to extract the shape and texture information of the feature points on the fracture edge. Finally, the feature points are optimized by minimum correction and image mosaic is carried out. In terms of image stitching, pre-registration is performed by finding the feature descriptors that are most similar to the edge of the optimum fracture surface profile. The matching operator is added to the overlapping region to obtain the corrected image, and the panoramic image mosaic of the image fracture surface is performed. The experimental results show that feature descriptor matching can ensure the integrity of the fracture, improve the matching accuracy, optimize the uneven deformation of the fracture, ensure the quality of image stitching, and reduce the degree of image distortion.

Under big data, a large number of features, as well as their complex data types, make traditional feature extraction and knowledge reasoning unable to adapt to new conditions. To solve these problems, this study proposes a museum big data feature extraction method based on a similarity mapping algorithm. Under the museum big data analysis, the museum big data text information is collected through web crawler technology. The web crawler is used to index the content of websites all across the Internet so that the museum websites can appear in search engine results and the collected text information is denoised and smoothed by a Gaussian filter to construct the processed text information set mapping matrix. The semantic similarity is computed according to the text word concept. Based on the calculation results, through word frequency and document probability inverse document frequency weight, the museum big data text information features are extracted. Simulation results show that the proposed method has high accuracy and short extraction time. Through the comparative analysis, it can be realized that this method not only solves the problems existing in traditional methods but also lays a foundation for the analysis of museum massive data.