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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Main Authors: Hadi Ali Hasan, Khalaf Abbas Zedan
Format: Article
Language:English
Published: EDP Sciences 2021-01-01
Series:E3S Web of Conferences
Subjects:
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/94/e3sconf_icge2021_04002.pdf
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author Hadi Ali Hasan
Khalaf Abbas Zedan
author_facet Hadi Ali Hasan
Khalaf Abbas Zedan
author_sort Hadi Ali Hasan
collection DOAJ
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.
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spelling doaj.art-894a2ee875d14f448083115694ca6aa22022-12-21T20:47:14ZengEDP SciencesE3S Web of Conferences2267-12422021-01-013180400210.1051/e3sconf/202131804002e3sconf_icge2021_04002Evaluation of Stereo Images MatchingHadi Ali Hasan0Khalaf Abbas Zedan1Civil Engineering Department, University of Technology-IraqCivil Engineering Department, University of Technology-IraqImage 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.https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/94/e3sconf_icge2021_04002.pdffeature detectionbriskfastharrismineigenstereo images matchingrmse
spellingShingle Hadi Ali Hasan
Khalaf Abbas Zedan
Evaluation of Stereo Images Matching
E3S Web of Conferences
feature detection
brisk
fast
harris
mineigen
stereo images matching
rmse
title 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_short Evaluation of Stereo Images Matching
title_sort evaluation of stereo images matching
topic feature detection
brisk
fast
harris
mineigen
stereo images matching
rmse
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/94/e3sconf_icge2021_04002.pdf
work_keys_str_mv AT hadialihasan evaluationofstereoimagesmatching
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