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A Robust Method for Automatic Panoramic UAV Image Mosaic

This paper introduces a robust method for panoramic unmanned aerial vehicle (UAV) image mosaic. In the traditional automatic panoramic image stitching method (Autostitch), it assumes that the camera rotates about its optical centre and the group of transformations the source images may undergo is a...

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Detalles Bibliográficos
Autores principales: Chen, Jun, Xu, Quan, Luo, Linbo, Wang, Yongtao, Wang, Shuchun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515254/
https://www.ncbi.nlm.nih.gov/pubmed/31013567
http://dx.doi.org/10.3390/s19081898
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author Chen, Jun
Xu, Quan
Luo, Linbo
Wang, Yongtao
Wang, Shuchun
author_facet Chen, Jun
Xu, Quan
Luo, Linbo
Wang, Yongtao
Wang, Shuchun
author_sort Chen, Jun
collection PubMed
description This paper introduces a robust method for panoramic unmanned aerial vehicle (UAV) image mosaic. In the traditional automatic panoramic image stitching method (Autostitch), it assumes that the camera rotates about its optical centre and the group of transformations the source images may undergo is a special group of homographies. It is rare to get such ideal data in reality. In particular, remote sensing images obtained by UAV do not satisfy such an ideal situation, where the images may not be on a plane yet and even may suffer from nonrigid changes, leading to poor mosaic results. To overcome the above mentioned challenges, in this paper a nonrigid matching algorithm is introduced to the mosaic system to generate accurate feature matching on remote sensing images. We also propose a new strategy for bundle adjustment to make the mosaic system suitable for the UAV image panoramic mosaic effect. Experimental results show that our method outperforms the traditional method and some of the latest methods in terms of visual effect.
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spelling pubmed-65152542019-05-30 A Robust Method for Automatic Panoramic UAV Image Mosaic Chen, Jun Xu, Quan Luo, Linbo Wang, Yongtao Wang, Shuchun Sensors (Basel) Article This paper introduces a robust method for panoramic unmanned aerial vehicle (UAV) image mosaic. In the traditional automatic panoramic image stitching method (Autostitch), it assumes that the camera rotates about its optical centre and the group of transformations the source images may undergo is a special group of homographies. It is rare to get such ideal data in reality. In particular, remote sensing images obtained by UAV do not satisfy such an ideal situation, where the images may not be on a plane yet and even may suffer from nonrigid changes, leading to poor mosaic results. To overcome the above mentioned challenges, in this paper a nonrigid matching algorithm is introduced to the mosaic system to generate accurate feature matching on remote sensing images. We also propose a new strategy for bundle adjustment to make the mosaic system suitable for the UAV image panoramic mosaic effect. Experimental results show that our method outperforms the traditional method and some of the latest methods in terms of visual effect. MDPI 2019-04-22 /pmc/articles/PMC6515254/ /pubmed/31013567 http://dx.doi.org/10.3390/s19081898 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Jun
Xu, Quan
Luo, Linbo
Wang, Yongtao
Wang, Shuchun
A Robust Method for Automatic Panoramic UAV Image Mosaic
title A Robust Method for Automatic Panoramic UAV Image Mosaic
title_full A Robust Method for Automatic Panoramic UAV Image Mosaic
title_fullStr A Robust Method for Automatic Panoramic UAV Image Mosaic
title_full_unstemmed A Robust Method for Automatic Panoramic UAV Image Mosaic
title_short A Robust Method for Automatic Panoramic UAV Image Mosaic
title_sort robust method for automatic panoramic uav image mosaic
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515254/
https://www.ncbi.nlm.nih.gov/pubmed/31013567
http://dx.doi.org/10.3390/s19081898
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