Imperial College London
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DrBennyLo

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Visiting Reader
 
 
 
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Contact

 

+44 (0)20 7594 0806benny.lo Website

 
 
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Location

 

Bessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Lo:2020:10.1109/tii.2019.2942831,
author = {Lo, FP-W and Sun, Y and Qiu, J and Lo, BPL},
doi = {10.1109/tii.2019.2942831},
journal = {IEEE Transactions on Industrial Informatics},
pages = {577--586},
title = {Point2Volume: A vision-based dietary assessment approach using view synthesis},
url = {http://dx.doi.org/10.1109/tii.2019.2942831},
volume = {16},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Lo,FP-W
AU  - Sun,Y
AU  - Qiu,J
AU  - Lo,BPL
DO  - 10.1109/tii.2019.2942831
EP  - 586
PY  - 2020///
SN  - 1551-3203
SP  - 577
TI  - Point2Volume: A vision-based dietary assessment approach using view synthesis
T2  - IEEE Transactions on Industrial Informatics
UR  - http://dx.doi.org/10.1109/tii.2019.2942831
UR  - https://ieeexplore.ieee.org/document/8853329
UR  - http://hdl.handle.net/10044/1/76639
VL  - 16
ER  -
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