Point2Volume: A vision-based dietary assessment approach using view synthesis
File(s)08853329.pdf (3.35 MB)
Published version
Author(s)
Lo, Frank P-W
Sun, Yingnan
Qiu, Jianing
Lo, Benny PL
Type
Journal Article
Abstract
Dietary assessment is an important tool for nutritional epidemiology studies. To assess the dietary intake, the common approach is to carry out 24-h dietary recall (24HR), a structured interview conducted by experienced dietitians. Due to the unconscious biases in such self-reporting methods, many research works have proposed the use of vision-based approaches to provide accurate and objective assessments. In this article, a novel vision-based method based on real-time three-dimensional (3-D) reconstruction and deep learning view synthesis is proposed to enable accurate portion size estimation of food items consumed. A point completion neural network is developed to complete partial point cloud of food items based on a single depth image or video captured from any convenient viewing position. Once 3-D models of food items are reconstructed, the food volume can be estimated through meshing. Compared to previous methods, our method has addressed several major challenges in vision-based dietary assessment, such as view occlusion and scale ambiguity, and it outperforms previous approaches in accurate portion size estimation.
Date Issued
2020-01-04
Date Acceptance
2019-09-13
Citation
IEEE Transactions on Industrial Informatics, 2020, 16 (1), pp.577-586
ISSN
1551-3203
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
577
End Page
586
Journal / Book Title
IEEE Transactions on Industrial Informatics
Volume
16
Issue
1
Copyright Statement
© 2020 Owner. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Identifier
https://ieeexplore.ieee.org/document/8853329
Subjects
Electrical & Electronic Engineering
08 Information and Computing Sciences
09 Engineering
10 Technology
Publication Status
Published
Date Publish Online
2019-09-30