Title: Image Stitching by Line-guided Local Warping with Global Similarity Constraint,
Abstract: Low-textured image stitching remains a challenging problem. It is difficult
to achieve good alignment and it is easy to break image structures due to
insufficient and unreliable point correspondences. Moreover, because of the
viewpoint variations between multiple images, the stitched images suffer from
projective distortions. To solve these problems, this paper presents a
line-guided local warping method with a global similarity constraint for image
stitching. Line features which serve well for geometric descriptions and scene
constraints, are employed to guide image stitching accurately. On one hand, the
line features are integrated into a local warping model through a designed
weight function. On the other hand, line features are adopted to impose strong
geometric constraints, including line correspondence and line colinearity, to
improve the stitching performance through mesh optimization. To mitigate
projective distortions, we adopt a global similarity constraint, which is
integrated with the projective warps via a designed weight strategy. This
constraint causes the final warp to slowly change from a projective to a
similarity transformation across the image. Finally, the images undergo a
two-stage alignment scheme that provides accurate alignment and reduces
projective distortion. We evaluate our method on a series of images and compare
it with several other methods. The experimental results demonstrate that the
proposed method provides a convincing stitching performance and that it
outperforms other state-of-the-art methods.