Image stitching based on FABEB and parallax tolerance
Jihui Qi, Shaozhong Cao, Changbo Xu, Yi Liu · 2022
In order to improve the efficiency of image stitching and solve the problem of visual artifacts, an image stitching algorithm based on FABEB and parallax tolerance is proposed. Firstly, the FABEB algorithm is proposed to efficiently obtain robust feature points by combining scale-space keypoint detection of FAST (Features From Accelerated Segment Test) and boosted efficient binary local image descriptor (BEBLID). In the feature point registration algorithm, KNN algorithm is first used for general screening. Marginalizing sample consensus algorithm (MAGSAC) is used for further screening, to get the geometric relation between the images. In order to solve the artifact problem in large parallax image stitching, this paper proposes an optimal seam-cutting fusion algorithm based on visual saliency. And it adds saliency feature into the seam-cutting strategy to make the results more consistent with the perception of human eyes. Experimental results show that compared with the traditional algorithm, the proposed image stitching strategy can extract and match feature points more efficiently and accurately, and eliminate stitching artifacts to get a better visual experience.