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Content-preserving image stitching with piecewise rectangular boundary constraints

Zhang, Yun, Lai, Yukun and Zhang, Fang-Lue 2020. Content-preserving image stitching with piecewise rectangular boundary constraints. IEEE Transactions on Visualization and Computer Graphics 10.1109/TVCG.2020.2965097

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Abstract

This paper proposes an approach to content-preserving image stitching with regular boundary constraints, which aims to stitch multiple images to generate a panoramic image with a piecewise rectangular boundary. Existing methods treat image stitching and rectangling as two separate steps, which may result in suboptimal results as the stitching process is not aware of the further warping needs for rectangling. We address these limitations by formulating image stitching with regular boundaries in a unified optimization. Starting from the initial stitching results produced by the traditional warping-based optimization, we obtain the irregular boundary from the warped meshes by polygon Boolean operations which robustly handle arbitrary mesh compositions. By analyzing the irregular boundary, we construct a piecewise rectangular boundary. Based on this, we further incorporate line and regular boundary preservation constraints into the image stitching framework, and conduct iterative optimization to obtain an optimal piecewise rectangular boundary. Thus we can make the boundary of the stitching results as close as possible to a rectangle, while reducing unwanted distortions. We further extend our method to video stitching, by integrating the temporal coherence into the optimization. Experiments show that our method efficiently produces visually pleasing panoramas with regular boundaries and unnoticeable distortions.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 1077-2626
Funders: The Royal Society
Date of First Compliant Deposit: 4 January 2020
Date of Acceptance: 3 January 2020
Last Modified: 25 Nov 2020 12:10
URI: http://orca.cf.ac.uk/id/eprint/128204

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