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A novel systems solution for accurate colorimetric measurement through smartphone-based augmented reality

Quantifying the colors of objects is useful in a wide range of applications, including medical diagnosis, agricultural monitoring, and food safety. Accurate colorimetric measurement of objects is a laborious process normally performed through a color matching test in the laboratory. A promising alte...

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Autores principales: Zhang, Guixiang, Song, Shuang, Panescu, Jenny, Shapiro, Nicholas, Dannemiller, Karen C., Qin, Rongjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270580/
https://www.ncbi.nlm.nih.gov/pubmed/37319291
http://dx.doi.org/10.1371/journal.pone.0287099
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author Zhang, Guixiang
Song, Shuang
Panescu, Jenny
Shapiro, Nicholas
Dannemiller, Karen C.
Qin, Rongjun
author_facet Zhang, Guixiang
Song, Shuang
Panescu, Jenny
Shapiro, Nicholas
Dannemiller, Karen C.
Qin, Rongjun
author_sort Zhang, Guixiang
collection PubMed
description Quantifying the colors of objects is useful in a wide range of applications, including medical diagnosis, agricultural monitoring, and food safety. Accurate colorimetric measurement of objects is a laborious process normally performed through a color matching test in the laboratory. A promising alternative is to use digital images for colorimetric measurement, due to their portability and ease of use. However, image-based measurements suffer from errors caused by the non-linear image formation process and unpredictable environmental lighting. Solutions to this problem often perform relative color correction among multiple images through discrete color reference boards, which may yield biased results due to the lack of continuous observation. In this paper, we propose a smartphone-based solution, that couples a designated color reference board with a novel color correction algorithm, to achieve accurate and absolute color measurements. Our color reference board contains multiple color stripes with continuous color sampling at the sides. A novel correction algorithm is proposed to utilize a first-order spatial varying regression model to perform the color correction, which leverages both the absolute color magnitude and scale to maximize the correction accuracy. The proposed algorithm is implemented as a “human-in-the-loop” smartphone application, where users are guided by an augmented reality scheme with a marker tracking module to take images at an angle that minimizes the impact of non-Lambertian reflectance. Our experimental results show that our colorimetric measurement is device independent and can reduce up to 90% color variance for images collected under different lighting conditions. In the application of reading pH values from test papers, we show that our system performs 200% better than human reading. The designed color reference board, the correction algorithm, and our augmented reality guiding approach form an integrated system as a novel solution to measure color with increased accuracy. This technique has the flexibility to improve color reading performance in systems beyond existing applications, evidenced by both qualitative and quantitative experiments on example applications such as pH-test reading.
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spelling pubmed-102705802023-06-16 A novel systems solution for accurate colorimetric measurement through smartphone-based augmented reality Zhang, Guixiang Song, Shuang Panescu, Jenny Shapiro, Nicholas Dannemiller, Karen C. Qin, Rongjun PLoS One Research Article Quantifying the colors of objects is useful in a wide range of applications, including medical diagnosis, agricultural monitoring, and food safety. Accurate colorimetric measurement of objects is a laborious process normally performed through a color matching test in the laboratory. A promising alternative is to use digital images for colorimetric measurement, due to their portability and ease of use. However, image-based measurements suffer from errors caused by the non-linear image formation process and unpredictable environmental lighting. Solutions to this problem often perform relative color correction among multiple images through discrete color reference boards, which may yield biased results due to the lack of continuous observation. In this paper, we propose a smartphone-based solution, that couples a designated color reference board with a novel color correction algorithm, to achieve accurate and absolute color measurements. Our color reference board contains multiple color stripes with continuous color sampling at the sides. A novel correction algorithm is proposed to utilize a first-order spatial varying regression model to perform the color correction, which leverages both the absolute color magnitude and scale to maximize the correction accuracy. The proposed algorithm is implemented as a “human-in-the-loop” smartphone application, where users are guided by an augmented reality scheme with a marker tracking module to take images at an angle that minimizes the impact of non-Lambertian reflectance. Our experimental results show that our colorimetric measurement is device independent and can reduce up to 90% color variance for images collected under different lighting conditions. In the application of reading pH values from test papers, we show that our system performs 200% better than human reading. The designed color reference board, the correction algorithm, and our augmented reality guiding approach form an integrated system as a novel solution to measure color with increased accuracy. This technique has the flexibility to improve color reading performance in systems beyond existing applications, evidenced by both qualitative and quantitative experiments on example applications such as pH-test reading. Public Library of Science 2023-06-15 /pmc/articles/PMC10270580/ /pubmed/37319291 http://dx.doi.org/10.1371/journal.pone.0287099 Text en © 2023 Zhang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhang, Guixiang
Song, Shuang
Panescu, Jenny
Shapiro, Nicholas
Dannemiller, Karen C.
Qin, Rongjun
A novel systems solution for accurate colorimetric measurement through smartphone-based augmented reality
title A novel systems solution for accurate colorimetric measurement through smartphone-based augmented reality
title_full A novel systems solution for accurate colorimetric measurement through smartphone-based augmented reality
title_fullStr A novel systems solution for accurate colorimetric measurement through smartphone-based augmented reality
title_full_unstemmed A novel systems solution for accurate colorimetric measurement through smartphone-based augmented reality
title_short A novel systems solution for accurate colorimetric measurement through smartphone-based augmented reality
title_sort novel systems solution for accurate colorimetric measurement through smartphone-based augmented reality
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270580/
https://www.ncbi.nlm.nih.gov/pubmed/37319291
http://dx.doi.org/10.1371/journal.pone.0287099
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