Phase Difference-3D Coordinate Mapping Model of Structural Light Imaging System Based on Extreme Learning Machine Network
To meet the requirements of high accuracy and high efficiency in three-dimensional (3D) measurement, a phase difference-3D coordinate mapping model is proposed based on extreme learning machine (ELM) network. First, the reconstruction model of the ideal measurement system is set following the geomet...
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doaj-ab12adaf1f9040c18b77d1f3ebd78e7c2021-03-30T01:51:26ZengIEEEIEEE Access2169-35362020-01-018689746898110.1109/ACCESS.2020.29862259058670Phase Difference-3D Coordinate Mapping Model of Structural Light Imaging System Based on Extreme Learning Machine NetworkShanshan Lv0https://orcid.org/0000-0003-0781-0412Mingshun Jiang1https://orcid.org/0000-0002-0031-7409Chenhui Su2https://orcid.org/0000-0002-7229-7443Lei Zhang3https://orcid.org/0000-0001-7732-153XFaye Zhang4https://orcid.org/0000-0001-6239-3231Qingmei Sui5https://orcid.org/0000-0002-7045-3967Lei Jia6https://orcid.org/0000-0002-5480-6814School of Control Science and Engineering, Shandong University, Jinan, ChinaSchool of Control Science and Engineering, Shandong University, Jinan, ChinaSchool of Control Science and Engineering, Shandong University, Jinan, ChinaSchool of Control Science and Engineering, Shandong University, Jinan, ChinaSchool of Control Science and Engineering, Shandong University, Jinan, ChinaSchool of Control Science and Engineering, Shandong University, Jinan, ChinaSchool of Control Science and Engineering, Shandong University, Jinan, ChinaTo meet the requirements of high accuracy and high efficiency in three-dimensional (3D) measurement, a phase difference-3D coordinate mapping model is proposed based on extreme learning machine (ELM) network. First, the reconstruction model of the ideal measurement system is set following the geometric structure of the system. Subsequently, by generalizing camera and world coordinates, a generalized measurement model is built. Lastly, ELM network is employed to solve the mapping coefficients. During measurement, only one phase difference map is required to complete the 3D reconstruction of the object, which simplifies the data processing process and saves time. The result indicates that the mean square errors (MSEs) of the X, Y and Z of the testing sample are 3.5955×10<sup>-4</sup> mm, 9.5113×10<sup>-4</sup> mm and 4.4×10<sup>-3</sup> mm, respectively. Moreover, the reconstruction experiments of objects with different geometric structures are performed to demonstrate the general application of the proposed method.https://ieeexplore.ieee.org/document/9058670/Phase differencestructural light3D reconstructionELM network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shanshan Lv Mingshun Jiang Chenhui Su Lei Zhang Faye Zhang Qingmei Sui Lei Jia |
spellingShingle |
Shanshan Lv Mingshun Jiang Chenhui Su Lei Zhang Faye Zhang Qingmei Sui Lei Jia Phase Difference-3D Coordinate Mapping Model of Structural Light Imaging System Based on Extreme Learning Machine Network IEEE Access Phase difference structural light 3D reconstruction ELM network |
author_facet |
Shanshan Lv Mingshun Jiang Chenhui Su Lei Zhang Faye Zhang Qingmei Sui Lei Jia |
author_sort |
Shanshan Lv |
title |
Phase Difference-3D Coordinate Mapping Model of Structural Light Imaging System Based on Extreme Learning Machine Network |
title_short |
Phase Difference-3D Coordinate Mapping Model of Structural Light Imaging System Based on Extreme Learning Machine Network |
title_full |
Phase Difference-3D Coordinate Mapping Model of Structural Light Imaging System Based on Extreme Learning Machine Network |
title_fullStr |
Phase Difference-3D Coordinate Mapping Model of Structural Light Imaging System Based on Extreme Learning Machine Network |
title_full_unstemmed |
Phase Difference-3D Coordinate Mapping Model of Structural Light Imaging System Based on Extreme Learning Machine Network |
title_sort |
phase difference-3d coordinate mapping model of structural light imaging system based on extreme learning machine network |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
To meet the requirements of high accuracy and high efficiency in three-dimensional (3D) measurement, a phase difference-3D coordinate mapping model is proposed based on extreme learning machine (ELM) network. First, the reconstruction model of the ideal measurement system is set following the geometric structure of the system. Subsequently, by generalizing camera and world coordinates, a generalized measurement model is built. Lastly, ELM network is employed to solve the mapping coefficients. During measurement, only one phase difference map is required to complete the 3D reconstruction of the object, which simplifies the data processing process and saves time. The result indicates that the mean square errors (MSEs) of the X, Y and Z of the testing sample are 3.5955×10<sup>-4</sup> mm, 9.5113×10<sup>-4</sup> mm and 4.4×10<sup>-3</sup> mm, respectively. Moreover, the reconstruction experiments of objects with different geometric structures are performed to demonstrate the general application of the proposed method. |
topic |
Phase difference structural light 3D reconstruction ELM network |
url |
https://ieeexplore.ieee.org/document/9058670/ |
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