Image stitching algorithm based on semantics-preserving warps

Generating naturally stitched images is a challenging task due to the presence of the parallax in images. In this paper, we propose a semantic-based method to handle the distortions and artifacts generated in the process of stitching. Firstly, a semantic-based random sample consensus algorithm is proposed to obtain the corresponding feature points, which can effectively eliminate mismatch points. The obtained corresponding features are used to align the image and establish the global structure information. Then, we propose semantics-preserving warps based on mesh optimization. The global structure information, semantic information and the corresponding features are combined to warp the images. Experimental results show that our algorithm can provide accurate alignment results while effectively reducing image distortions in both overlapping and non-overlapping regions.