图像拼接
重影
图像扭曲
视差
计算机视觉
计算机科学
失真(音乐)
人工智能
透视图(图形)
复制品
计算机网络
艺术
视觉艺术
放大器
带宽(计算)
作者
Wanli Xue,Weilun Xie,Yao Zhang,Shengyong Chen
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology
[Institute of Electrical and Electronics Engineers]
日期:2021-02-13
卷期号:32 (1): 253-261
被引量:14
标识
DOI:10.1109/tcsvt.2021.3058655
摘要
Parallax tolerance is a fundamental problem in image stitching. To solve this problem, increasing effort has been devoted to the spatially varying and seam cutting. However, there still exist some issues that need to be adequately addressed. First, the implementation of the spatially varying warping requires a restricted premise that the overlapping region can be aligned: if this premise is not met, ghosting caused by misalignment will emerge. In addition, the spatially varying warping may cause distortion due to the issue of inconsistent homographies. Second, conventional seam cutting will lead to objects being cropped and duplicated. Therefore, in this paper, we propose a stable framework for stitching images: the framework consists of a uniform linear structure model that is able to mitigate the distortion of projection and perspective, while preserving the structures of objects in the non-overlapping region; and a stable hybrid actor-critic that estimates stable seam measurements in the overlapping region to diminish the parallax. Comparison experiments show that the proposed method is superior to some conventional methods with respect to mitigating ghosting and preserving structure.
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