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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Main Authors: Shanshan Lv, Mingshun Jiang, Chenhui Su, Lei Zhang, Faye Zhang, Qingmei Sui, Lei Jia
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9058670/
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spelling 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&#x00D7;10<sup>-4</sup> mm, 9.5113&#x00D7;10<sup>-4</sup> mm and 4.4&#x00D7;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&#x00D7;10<sup>-4</sup> mm, 9.5113&#x00D7;10<sup>-4</sup> mm and 4.4&#x00D7;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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