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Structured Light Three-Dimensional Measurement Based on Machine Learning
The three-dimensional measurement of structured light is commonly used and has widespread applications in many industries. In this study, machine learning is used for structured light 3D measurement to recover the phase distribution of the measured object by employing two machine learning models. Wi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679525/ https://www.ncbi.nlm.nih.gov/pubmed/31340449 http://dx.doi.org/10.3390/s19143229 |
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author | Zhong, Chuqian Gao, Zhan Wang, Xu Shao, Shuangyun Gao, Chenjia |
author_facet | Zhong, Chuqian Gao, Zhan Wang, Xu Shao, Shuangyun Gao, Chenjia |
author_sort | Zhong, Chuqian |
collection | PubMed |
description | The three-dimensional measurement of structured light is commonly used and has widespread applications in many industries. In this study, machine learning is used for structured light 3D measurement to recover the phase distribution of the measured object by employing two machine learning models. Without phase shift, the measurement operational complexity and computation time decline renders real-time measurement possible. Finally, a grating-based structured light measurement system is constructed, and machine learning is used to recover the phase. The calculated phase of distribution is wrapped in only one dimension and not in two dimensions, as in other methods. The measurement error is observed to be under 1%. |
format | Online Article Text |
id | pubmed-6679525 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66795252019-08-19 Structured Light Three-Dimensional Measurement Based on Machine Learning Zhong, Chuqian Gao, Zhan Wang, Xu Shao, Shuangyun Gao, Chenjia Sensors (Basel) Article The three-dimensional measurement of structured light is commonly used and has widespread applications in many industries. In this study, machine learning is used for structured light 3D measurement to recover the phase distribution of the measured object by employing two machine learning models. Without phase shift, the measurement operational complexity and computation time decline renders real-time measurement possible. Finally, a grating-based structured light measurement system is constructed, and machine learning is used to recover the phase. The calculated phase of distribution is wrapped in only one dimension and not in two dimensions, as in other methods. The measurement error is observed to be under 1%. MDPI 2019-07-23 /pmc/articles/PMC6679525/ /pubmed/31340449 http://dx.doi.org/10.3390/s19143229 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhong, Chuqian Gao, Zhan Wang, Xu Shao, Shuangyun Gao, Chenjia Structured Light Three-Dimensional Measurement Based on Machine Learning |
title | Structured Light Three-Dimensional Measurement Based on Machine Learning |
title_full | Structured Light Three-Dimensional Measurement Based on Machine Learning |
title_fullStr | Structured Light Three-Dimensional Measurement Based on Machine Learning |
title_full_unstemmed | Structured Light Three-Dimensional Measurement Based on Machine Learning |
title_short | Structured Light Three-Dimensional Measurement Based on Machine Learning |
title_sort | structured light three-dimensional measurement based on machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679525/ https://www.ncbi.nlm.nih.gov/pubmed/31340449 http://dx.doi.org/10.3390/s19143229 |
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