Research on multi-view image stitching method in mine

Abstract

In order to solve the problems of small field of view and unclear detail features of monitoring images in coal mines, a multi-view image stitching method in mine is proposed. Firstly, an improved adaptive histogram equalization method with contrast limitation is used to pre-process the images to highlight the image details and improve the contrast. Secondly, the ORB algorithm is used to extract the image feature points, and the improved Brief algorithm is used to calculate the feature descriptors. Thirdly, the K-nearest neighbor(KNN) algorithm is used to achieve rough matching of feature point pairs, and the random sample consensus(RANSAC) algorithm is used to filter and eliminate the mismatched feature point pairs, and the optimal perspective transformation matrix is solved to transform the coordinates of the pixel points of the image to be matched. Finally, the hat function weighted average fusion algorithm is used to stitch and fuse the fixed image and the image to be matched. The experimental results show that compared with the speeded up robust features(SIFT) and KAZE algorithms, the ORB algorithm reduces the number of feature points extracted for a single image by 48% and 33% respectively, which improves the effective feature point extraction capability. The feature point extraction time is reduced by 17% and 34% respectively, which improves the calculation efficiency. The images stitched by this method avoid the phenomenon of cracks and black lines at the joints, the image transition is natural, and the definition is high.

Keywords

• mine monitoring images; multi-view images; image stitching; feature point extraction; feature matching; image fusion