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Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System

It is well known that many chronic diseases are associated with unhealthy diet. Although improving diet is critical, adopting a healthy diet is difficult despite its benefits being well understood. Technology is needed to allow an assessment of dietary intake accurately and easily in real-world sett...

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Autores principales: Yang, Zhengeng, Yu, Hongshan, Cao, Shunxin, Xu, Qi, Yuan, Ding, Zhang, Hong, Jia, Wenyan, Mao, Zhi-Hong, Sun, Mingui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8455030/
https://www.ncbi.nlm.nih.gov/pubmed/34552763
http://dx.doi.org/10.3390/electronics10131556
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author Yang, Zhengeng
Yu, Hongshan
Cao, Shunxin
Xu, Qi
Yuan, Ding
Zhang, Hong
Jia, Wenyan
Mao, Zhi-Hong
Sun, Mingui
author_facet Yang, Zhengeng
Yu, Hongshan
Cao, Shunxin
Xu, Qi
Yuan, Ding
Zhang, Hong
Jia, Wenyan
Mao, Zhi-Hong
Sun, Mingui
author_sort Yang, Zhengeng
collection PubMed
description It is well known that many chronic diseases are associated with unhealthy diet. Although improving diet is critical, adopting a healthy diet is difficult despite its benefits being well understood. Technology is needed to allow an assessment of dietary intake accurately and easily in real-world settings so that effective intervention to manage being overweight, obesity, and related chronic diseases can be developed. In recent years, new wearable imaging and computational technologies have emerged. These technologies are capable of performing objective and passive dietary assessments with a much simplified procedure than traditional questionnaires. However, a critical task is required to estimate the portion size (in this case, the food volume) from a digital image. Currently, this task is very challenging because the volumetric information in the two-dimensional images is incomplete, and the estimation involves a great deal of imagination, beyond the capacity of the traditional image processing algorithms. In this work, we present a novel Artificial Intelligent (AI) system to mimic the thinking of dietitians who use a set of common objects as gauges (e.g., a teaspoon, a golf ball, a cup, and so on) to estimate the portion size. Specifically, our human-mimetic system “mentally” gauges the volume of food using a set of internal reference volumes that have been learned previously. At the output, our system produces a vector of probabilities of the food with respect to the internal reference volumes. The estimation is then completed by an “intelligent guess”, implemented by an inner product between the probability vector and the reference volume vector. Our experiments using both virtual and real food datasets have shown accurate volume estimation results.
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spelling pubmed-84550302021-09-21 Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System Yang, Zhengeng Yu, Hongshan Cao, Shunxin Xu, Qi Yuan, Ding Zhang, Hong Jia, Wenyan Mao, Zhi-Hong Sun, Mingui Electronics (Basel) Article It is well known that many chronic diseases are associated with unhealthy diet. Although improving diet is critical, adopting a healthy diet is difficult despite its benefits being well understood. Technology is needed to allow an assessment of dietary intake accurately and easily in real-world settings so that effective intervention to manage being overweight, obesity, and related chronic diseases can be developed. In recent years, new wearable imaging and computational technologies have emerged. These technologies are capable of performing objective and passive dietary assessments with a much simplified procedure than traditional questionnaires. However, a critical task is required to estimate the portion size (in this case, the food volume) from a digital image. Currently, this task is very challenging because the volumetric information in the two-dimensional images is incomplete, and the estimation involves a great deal of imagination, beyond the capacity of the traditional image processing algorithms. In this work, we present a novel Artificial Intelligent (AI) system to mimic the thinking of dietitians who use a set of common objects as gauges (e.g., a teaspoon, a golf ball, a cup, and so on) to estimate the portion size. Specifically, our human-mimetic system “mentally” gauges the volume of food using a set of internal reference volumes that have been learned previously. At the output, our system produces a vector of probabilities of the food with respect to the internal reference volumes. The estimation is then completed by an “intelligent guess”, implemented by an inner product between the probability vector and the reference volume vector. Our experiments using both virtual and real food datasets have shown accurate volume estimation results. 2021-06-28 2021-07 /pmc/articles/PMC8455030/ /pubmed/34552763 http://dx.doi.org/10.3390/electronics10131556 Text en https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Zhengeng
Yu, Hongshan
Cao, Shunxin
Xu, Qi
Yuan, Ding
Zhang, Hong
Jia, Wenyan
Mao, Zhi-Hong
Sun, Mingui
Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System
title Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System
title_full Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System
title_fullStr Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System
title_full_unstemmed Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System
title_short Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System
title_sort human-mimetic estimation of food volume from a single-view rgb image using an ai system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8455030/
https://www.ncbi.nlm.nih.gov/pubmed/34552763
http://dx.doi.org/10.3390/electronics10131556
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