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Rotation Estimation: A Closed-Form Solution Using Spherical Moments †

Photometric moments are global descriptors of an image that can be used to recover motion information. This paper uses spherical photometric moments for a closed form estimation of 3D rotations from images. Since the used descriptors are global and not of the geometrical kind, they allow to avoid im...

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Detalles Bibliográficos
Autores principales: Hadj-Abdelkader, Hicham, Tahri, Omar, Benseddik, Houssem-Eddine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891670/
https://www.ncbi.nlm.nih.gov/pubmed/31739484
http://dx.doi.org/10.3390/s19224958
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author Hadj-Abdelkader, Hicham
Tahri, Omar
Benseddik, Houssem-Eddine
author_facet Hadj-Abdelkader, Hicham
Tahri, Omar
Benseddik, Houssem-Eddine
author_sort Hadj-Abdelkader, Hicham
collection PubMed
description Photometric moments are global descriptors of an image that can be used to recover motion information. This paper uses spherical photometric moments for a closed form estimation of 3D rotations from images. Since the used descriptors are global and not of the geometrical kind, they allow to avoid image processing as features extraction, matching, and tracking. The proposed scheme based on spherical projection can be used for the different vision sensors obeying the central unified model: conventional, fisheye, and catadioptric. Experimental results using both synthetic data and real images in different scenarios are provided to show the efficiency of the proposed method.
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spelling pubmed-68916702019-12-12 Rotation Estimation: A Closed-Form Solution Using Spherical Moments † Hadj-Abdelkader, Hicham Tahri, Omar Benseddik, Houssem-Eddine Sensors (Basel) Article Photometric moments are global descriptors of an image that can be used to recover motion information. This paper uses spherical photometric moments for a closed form estimation of 3D rotations from images. Since the used descriptors are global and not of the geometrical kind, they allow to avoid image processing as features extraction, matching, and tracking. The proposed scheme based on spherical projection can be used for the different vision sensors obeying the central unified model: conventional, fisheye, and catadioptric. Experimental results using both synthetic data and real images in different scenarios are provided to show the efficiency of the proposed method. MDPI 2019-11-14 /pmc/articles/PMC6891670/ /pubmed/31739484 http://dx.doi.org/10.3390/s19224958 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
Hadj-Abdelkader, Hicham
Tahri, Omar
Benseddik, Houssem-Eddine
Rotation Estimation: A Closed-Form Solution Using Spherical Moments †
title Rotation Estimation: A Closed-Form Solution Using Spherical Moments †
title_full Rotation Estimation: A Closed-Form Solution Using Spherical Moments †
title_fullStr Rotation Estimation: A Closed-Form Solution Using Spherical Moments †
title_full_unstemmed Rotation Estimation: A Closed-Form Solution Using Spherical Moments †
title_short Rotation Estimation: A Closed-Form Solution Using Spherical Moments †
title_sort rotation estimation: a closed-form solution using spherical moments †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891670/
https://www.ncbi.nlm.nih.gov/pubmed/31739484
http://dx.doi.org/10.3390/s19224958
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