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Image Quality Assessment Using Similar Scene as Reference Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Natural Image Stitching with the Global Similarity Prior

Authors : Yu-Sheng Chen, Yung-Yu Chuang

Published in: Computer Vision – ECCV 2016

Publisher: Springer International Publishing

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Abstract

This paper proposes a method for stitching multiple images together so that the stitched image looks as natural as possible. Our method adopts the local warp model and guides the warping of each image with a grid mesh. An objective function is designed for specifying the desired characteristics of the warps. In addition to good alignment and minimal local distortion, we add a global similarity prior in the objective function. This prior constrains the warp of each image so that it resembles a similarity transformation as a whole. The selection of the similarity transformation is crucial to the naturalness of the results. We propose methods for selecting the proper scale and rotation for each image. The warps of all images are solved together for minimizing the distortion globally. A comprehensive evaluation shows that the proposed method consistently outperforms several state-of-the-art methods, including AutoStitch, APAP, SPHP and ANNAP.

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previous chapter A Minimal Solution for Non-perspective Pose Estimation from Line Correspondences
next chapter Minimal Solvers for Generalized Pose and Scale Estimation from Two Rays and One Point
Appendix
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Footnotes
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Metadata
Title
Natural Image Stitching with the Global Similarity Prior
Authors
Yu-Sheng Chen
Yung-Yu Chuang
Copyright Year
2016
Book
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46453-4

Electronic ISBN: 978-3-319-46454-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46454-1_12

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