Evaluation of Stereo Images Matching

Image matching and finding correspondence between a stereo image pair is an essential task in digital photogrammetry and computer vision. Stereo images represent the same scene from two different perspectives, and therefore they typically contain a high degree of redundancy. This paper includes an e...

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Autores principales:	Hadi Ali Hasan, Khalaf Abbas Zedan
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	feature detection brisk fast harris mineigen stereo images matching rmse Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/894a2ee875d14f448083115694ca6aa2
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spelling	oai:doaj.org-article:894a2ee875d14f448083115694ca6aa22021-11-12T11:43:51ZEvaluation of Stereo Images Matching2267-124210.1051/e3sconf/202131804002https://doaj.org/article/894a2ee875d14f448083115694ca6aa22021-01-01T00:00:00Zhttps://www.e3s-conferences.org/articles/e3sconf/pdf/2021/94/e3sconf_icge2021_04002.pdfhttps://doaj.org/toc/2267-1242Image matching and finding correspondence between a stereo image pair is an essential task in digital photogrammetry and computer vision. Stereo images represent the same scene from two different perspectives, and therefore they typically contain a high degree of redundancy. This paper includes an evaluation of implementing manual as well as auto-match between a pair of images that acquired with an overlapped area. Particular target points are selected to be matched manually (22 target points). Auto-matching, based on feature-based matching (FBM) method, has been applied to these target points by using BRISK, FAST, Harris, and MinEigen algorithms. Auto matching is conducted with two main phases: extraction (detection and description) and matching features. The matching techniques used by the prevalent algorithms depend on local point (corner) features. Also, the performance of the algorithms is assessed according to the results obtained from various criteria, such as the number of auto-matched points and the target points that auto-matched. This study aims to determine and evaluate the total root mean square error (RMSE) by comparing coordinates of manual matched target points with those obtained from auto-matching by each of the algorithms. According to the experimental results, the BRISK algorithm gives the higher number of auto-matched points, which equals 2942, while the Harris algorithm gives 378 points representing the lowest number of auto-matched points. All target points are auto-matched with BRISK and FAST algorithms, while 3 and 9 target points only auto-matched with Harris and MinEigen algorithms, respectively. Total RMSE in its minimum value is given by FAST and manual match in the first image, it is 0.002651206 mm, and Harris and manual match provide the minimum value of total RMSE in the second image is 0.002399477 mm.Hadi Ali HasanKhalaf Abbas ZedanEDP Sciencesarticlefeature detectionbriskfastharrismineigenstereo images matchingrmseEnvironmental sciencesGE1-350ENFRE3S Web of Conferences, Vol 318, p 04002 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	feature detection brisk fast harris mineigen stereo images matching rmse Environmental sciences GE1-350
spellingShingle	feature detection brisk fast harris mineigen stereo images matching rmse Environmental sciences GE1-350 Hadi Ali Hasan Khalaf Abbas Zedan Evaluation of Stereo Images Matching
description	Image matching and finding correspondence between a stereo image pair is an essential task in digital photogrammetry and computer vision. Stereo images represent the same scene from two different perspectives, and therefore they typically contain a high degree of redundancy. This paper includes an evaluation of implementing manual as well as auto-match between a pair of images that acquired with an overlapped area. Particular target points are selected to be matched manually (22 target points). Auto-matching, based on feature-based matching (FBM) method, has been applied to these target points by using BRISK, FAST, Harris, and MinEigen algorithms. Auto matching is conducted with two main phases: extraction (detection and description) and matching features. The matching techniques used by the prevalent algorithms depend on local point (corner) features. Also, the performance of the algorithms is assessed according to the results obtained from various criteria, such as the number of auto-matched points and the target points that auto-matched. This study aims to determine and evaluate the total root mean square error (RMSE) by comparing coordinates of manual matched target points with those obtained from auto-matching by each of the algorithms. According to the experimental results, the BRISK algorithm gives the higher number of auto-matched points, which equals 2942, while the Harris algorithm gives 378 points representing the lowest number of auto-matched points. All target points are auto-matched with BRISK and FAST algorithms, while 3 and 9 target points only auto-matched with Harris and MinEigen algorithms, respectively. Total RMSE in its minimum value is given by FAST and manual match in the first image, it is 0.002651206 mm, and Harris and manual match provide the minimum value of total RMSE in the second image is 0.002399477 mm.
format	article
author	Hadi Ali Hasan Khalaf Abbas Zedan
author_facet	Hadi Ali Hasan Khalaf Abbas Zedan
author_sort	Hadi Ali Hasan
title	Evaluation of Stereo Images Matching
title_short	Evaluation of Stereo Images Matching
title_full	Evaluation of Stereo Images Matching
title_fullStr	Evaluation of Stereo Images Matching
title_full_unstemmed	Evaluation of Stereo Images Matching
title_sort	evaluation of stereo images matching
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/894a2ee875d14f448083115694ca6aa2
work_keys_str_mv	AT hadialihasan evaluationofstereoimagesmatching AT khalafabbaszedan evaluationofstereoimagesmatching
_version_	1718430630165872640

Evaluation of Stereo Images Matching

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