Enhancing Image Stitching Algorithms with SIFT Feature Detection
Mingkai Wang · Applied and Computational Engineering · 2024
Image stitching technology plays a significant role in the fields of computer vision and image processing, with applications ranging from panoramic photography to virtual reality (VR), augmented reality (AR), medical diagnostics, and autonomous vehicle technology. As technology advances, the demand for high-quality, real-time panoramic images provided by image stitching technology continues to grow. This study aims to implement an image stitching method based on the Scale-Invariant Feature Transform (SIFT) feature point detection algorithm, combined with the Random Sampling Consensus (RANSAC) algorithm and the calculation of the homography matrix to automatically stitch two images. This paper elaborates on the entire process of image feature extraction, feature matching, homography matrix calculation, and image fusion, and compares different fusion modes. The experimental results show that the method can achieve seamless image stitching in some cases, its performance in complex scenes such as crowds or traffic flows is average. This study provides new perspectives and methods for the application of image stitching technology.