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Machine Vision and Applications

Erschienen in:

07.12.2017 | Original Paper

Robust spatial–temporal Bayesian view synthesis for video stitching with occlusion handling

verfasst von: Jianmei Su, Hong Cheng, Lu Yang, Ao Luo

Erschienen in: Machine Vision and Applications | Ausgabe 2/2018

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Abstract

Occlusion is visible in only one frame and cannot be seen in the other frame which is a vital challenge in video stitching. Occlusion always brings ghost artifacts in the blended area. Meanwhile, the traditional image stitching approaches ignore temporal consistency and cannot avoid flicking problem. To solve these challenges, we propose a unified framework in which the stitching quality and stabilization both perform well. Specifically, we explicitly detect the potential occlusion regions to indicate blending information. Then, based on the occlusion maps, we choose a proper strip in the overlapped region as the blending area. With spatial–temporal Bayesian view synthesis, spatial ghost-like artifacts can be significantly eliminated and the output videos can be kept stable. The experimental results show the out performance of the proposed approach compared to state-of-the-art approaches.

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Titel: Robust spatial–temporal Bayesian view synthesis for video stitching with occlusion handling
verfasst von: Jianmei Su
Hong Cheng
Lu Yang
Ao Luo
Publikationsdatum: 07.12.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Machine Vision and Applications / Ausgabe 2/2018
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI: https://doi.org/10.1007/s00138-017-0888-5

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