Locally warping based image stitching by imposing line constraints
Warping based image stitching methods often suffer from perspective variations among multiple images and lead to shape and perspective distortions in stitching results. Moreover, they also quickly lose their efficiency in low textured images, due to the lack of reliable point correspondences. To solve these problems, this paper presents a locally warping based image stitching by imposing line constraints. First, a two stage alignment scheme with line constraints is introduced to achieve accurate alignment. More precisely, line features are adopted as alignment constraints to jointly estimate local homographies with point correspondences, which provides strong correspondences especially in low textured cases. Then line constraints are also imposed to the content preserving warping framework to further reduce alignment errors and preserve image structures. Second, in order to preserve shape and perspective information, a global similarity transform is introduced to mitigate projective distortions. Experimental results demonstrate the efficiency of our method, which yields more encouraging image stitching results in contrast with state of the art methods.
2016 23rd International Conference on Pattern Recognition (ICPR)