Region-based parallax-tolerant image stitching
Chenxu Zhao, Hai Zhang, Jieling Chen, Wenxing Fu · 2019
Image Stitching with large parallax has always been a challenging task, and accurate image alignment is critical for stitching results. In this paper, an image stitching method based on superpixel segmentation regions is proposed. To solve the problem of insufficient matching feature points under large parallax, an improved multi-plane RANSAC method is used to improve the robustness of matching feature selection algorithm. In terms of image alignment, a mesh optimization method with the global similarity prior is adopted, and a superpixel-based segmentation method is used to obtain reasonable matching points and global similarity transformation parameters. A standard seam-cutting algorithm is finally used to compose images together. Experiments show that the proposed method can effectively improve the performance of image stitching in complex scenes with large parallax.