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具有分段矩形边界约束的内容保留图像拼接

Content-Preserving Image Stitching With Piecewise Rectangular Boundary Constraints.

作者信息

Zhang Yun, Lai Yu-Kun, Zhang Fang-Lue

出版信息

IEEE Trans Vis Comput Graph. 2021 Jul;27(7):3198-3212. doi: 10.1109/TVCG.2020.2965097. Epub 2021 May 27.

Abstract

This article proposes an approach to content-preserving image stitching with regular boundary constraints, which aims to stitch multiple images to generate a panoramic image with piecewise rectangular boundaries. 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 framework. Starting from the initial stitching result produced by 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 optimizations to obtain an optimal piecewise rectangular boundary. Thus we can make the boundary of the stitching result 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.

摘要

本文提出了一种具有规则边界约束的内容保留图像拼接方法,旨在拼接多幅图像以生成具有分段矩形边界的全景图像。现有方法将图像拼接和矩形化视为两个独立的步骤,由于拼接过程未考虑矩形化所需的进一步扭曲,可能会导致次优结果。我们通过在统一的优化框架中制定具有规则边界的图像拼接来解决这些限制。从传统基于扭曲的优化产生的初始拼接结果开始,我们通过多边形布尔运算从扭曲的网格中获得不规则边界,该运算能够稳健地处理任意网格组合。通过分析不规则边界,我们构建一个分段矩形边界。基于此,我们进一步将线条和规则边界保留约束纳入图像拼接框架,并进行迭代优化以获得最佳分段矩形边界。这样我们可以使拼接结果的边界尽可能接近矩形,同时减少不必要的失真。我们通过将时间连贯性集成到优化中,进一步将我们的方法扩展到视频拼接。实验表明,我们的方法能够高效地生成具有规则边界且失真不明显的视觉上令人愉悦的全景图。

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