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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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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. |
format | Online Article Text |
id | pubmed-8455030 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
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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