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Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence
Epipolar resampling is the procedure of eliminating vertical disparity between stereo images. Due to its importance, many methods have been developed in the computer vision and photogrammetry field. However, we argue that epipolar resampling of image sequences, instead of a single pair, has not been...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4813987/ https://www.ncbi.nlm.nih.gov/pubmed/27011186 http://dx.doi.org/10.3390/s16030412 |
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author | Kim, Jae-In Kim, Taejung |
author_facet | Kim, Jae-In Kim, Taejung |
author_sort | Kim, Jae-In |
collection | PubMed |
description | Epipolar resampling is the procedure of eliminating vertical disparity between stereo images. Due to its importance, many methods have been developed in the computer vision and photogrammetry field. However, we argue that epipolar resampling of image sequences, instead of a single pair, has not been studied thoroughly. In this paper, we compare epipolar resampling methods developed in both fields for handling image sequences. Firstly we briefly review the uncalibrated and calibrated epipolar resampling methods developed in computer vision and photogrammetric epipolar resampling methods. While it is well known that epipolar resampling methods developed in computer vision and in photogrammetry are mathematically identical, we also point out differences in parameter estimation between them. Secondly, we tested representative resampling methods in both fields and performed an analysis. We showed that for epipolar resampling of a single image pair all uncalibrated and photogrammetric methods tested could be used. More importantly, we also showed that, for image sequences, all methods tested, except the photogrammetric Bayesian method, showed significant variations in epipolar resampling performance. Our results indicate that the Bayesian method is favorable for epipolar resampling of image sequences. |
format | Online Article Text |
id | pubmed-4813987 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-48139872016-04-06 Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence Kim, Jae-In Kim, Taejung Sensors (Basel) Article Epipolar resampling is the procedure of eliminating vertical disparity between stereo images. Due to its importance, many methods have been developed in the computer vision and photogrammetry field. However, we argue that epipolar resampling of image sequences, instead of a single pair, has not been studied thoroughly. In this paper, we compare epipolar resampling methods developed in both fields for handling image sequences. Firstly we briefly review the uncalibrated and calibrated epipolar resampling methods developed in computer vision and photogrammetric epipolar resampling methods. While it is well known that epipolar resampling methods developed in computer vision and in photogrammetry are mathematically identical, we also point out differences in parameter estimation between them. Secondly, we tested representative resampling methods in both fields and performed an analysis. We showed that for epipolar resampling of a single image pair all uncalibrated and photogrammetric methods tested could be used. More importantly, we also showed that, for image sequences, all methods tested, except the photogrammetric Bayesian method, showed significant variations in epipolar resampling performance. Our results indicate that the Bayesian method is favorable for epipolar resampling of image sequences. MDPI 2016-03-22 /pmc/articles/PMC4813987/ /pubmed/27011186 http://dx.doi.org/10.3390/s16030412 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kim, Jae-In Kim, Taejung Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence |
title | Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence |
title_full | Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence |
title_fullStr | Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence |
title_full_unstemmed | Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence |
title_short | Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence |
title_sort | comparison of computer vision and photogrammetric approaches for epipolar resampling of image sequence |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4813987/ https://www.ncbi.nlm.nih.gov/pubmed/27011186 http://dx.doi.org/10.3390/s16030412 |
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