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Portable and low-cost hologram verification module using a snapshot-based hyperspectral imaging algorithm
One of the challenges in differentiating a duplicate hologram from an original one is reflectivity. A slight change in lighting condition will completely change the reflection pattern exhibited by a hologram, and consequently, a standardized duplicate hologram detector has not yet been created. In t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9630442/ https://www.ncbi.nlm.nih.gov/pubmed/36323727 http://dx.doi.org/10.1038/s41598-022-22424-5 |
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author | Mukundan, Arvind Tsao, Yu-Ming Lin, Fen-Chi Wang, Hsiang-Chen |
author_facet | Mukundan, Arvind Tsao, Yu-Ming Lin, Fen-Chi Wang, Hsiang-Chen |
author_sort | Mukundan, Arvind |
collection | PubMed |
description | One of the challenges in differentiating a duplicate hologram from an original one is reflectivity. A slight change in lighting condition will completely change the reflection pattern exhibited by a hologram, and consequently, a standardized duplicate hologram detector has not yet been created. In this study, a portable and low-cost snapshot hyperspectral imaging (HSI) algorithm-based housing module for differentiating between original and duplicate holograms was proposed. The module consisted of a Raspberry Pi 4 processor, a Raspberry Pi camera, a display, and a light-emitting diode lighting system with a dimmer. A visible HSI algorithm that could convert an RGB image captured by the Raspberry Pi camera into a hyperspectral image was established. A specific region of interest was selected from the spectral image and mean gray value (MGV) and reflectivity were measured. Results suggested that shorter wavelengths are the most suitable for differentiating holograms when using MGV as the parameter for classification, while longer wavelengths are the most suitable when using reflectivity. The key features of this design include low cost, simplicity, lack of moving parts, and no requirement for an additional decoding key. |
format | Online Article Text |
id | pubmed-9630442 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96304422022-11-04 Portable and low-cost hologram verification module using a snapshot-based hyperspectral imaging algorithm Mukundan, Arvind Tsao, Yu-Ming Lin, Fen-Chi Wang, Hsiang-Chen Sci Rep Article One of the challenges in differentiating a duplicate hologram from an original one is reflectivity. A slight change in lighting condition will completely change the reflection pattern exhibited by a hologram, and consequently, a standardized duplicate hologram detector has not yet been created. In this study, a portable and low-cost snapshot hyperspectral imaging (HSI) algorithm-based housing module for differentiating between original and duplicate holograms was proposed. The module consisted of a Raspberry Pi 4 processor, a Raspberry Pi camera, a display, and a light-emitting diode lighting system with a dimmer. A visible HSI algorithm that could convert an RGB image captured by the Raspberry Pi camera into a hyperspectral image was established. A specific region of interest was selected from the spectral image and mean gray value (MGV) and reflectivity were measured. Results suggested that shorter wavelengths are the most suitable for differentiating holograms when using MGV as the parameter for classification, while longer wavelengths are the most suitable when using reflectivity. The key features of this design include low cost, simplicity, lack of moving parts, and no requirement for an additional decoding key. Nature Publishing Group UK 2022-11-02 /pmc/articles/PMC9630442/ /pubmed/36323727 http://dx.doi.org/10.1038/s41598-022-22424-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Mukundan, Arvind Tsao, Yu-Ming Lin, Fen-Chi Wang, Hsiang-Chen Portable and low-cost hologram verification module using a snapshot-based hyperspectral imaging algorithm |
title | Portable and low-cost hologram verification module using a snapshot-based hyperspectral imaging algorithm |
title_full | Portable and low-cost hologram verification module using a snapshot-based hyperspectral imaging algorithm |
title_fullStr | Portable and low-cost hologram verification module using a snapshot-based hyperspectral imaging algorithm |
title_full_unstemmed | Portable and low-cost hologram verification module using a snapshot-based hyperspectral imaging algorithm |
title_short | Portable and low-cost hologram verification module using a snapshot-based hyperspectral imaging algorithm |
title_sort | portable and low-cost hologram verification module using a snapshot-based hyperspectral imaging algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9630442/ https://www.ncbi.nlm.nih.gov/pubmed/36323727 http://dx.doi.org/10.1038/s41598-022-22424-5 |
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