SEAGULL: Seam-Guided Local Alignment for Parallax-Tolerant Image Stitching

Kaimo Lin, Nianjuan Jiang, Loong-Fah Cheong, Minh N. Do, Jiangbo Lu

Abstract: Image stitching with large parallax is a challenging problem. Global alignment usually introduces noticeable artifacts. A common strategy is to perform partial alignment to facilitate the search for a good seam for stitching. Different from existing approaches where the seam estimation process is performed sequentially after alignment, we explicitly use the estimated seam to guide the process of optimizing local alignment so that the seam quality gets improved over each iteration. Furthermore, a novel structure-preserving warping method is introduced to preserve salient curve and line structures during the warping. These measures substantially improve the effectiveness of our method in dealing with a wide range of challenging images with large parallax.