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.
Highlights
A RECENT National Health Service (NHS) survey [1] disclosed that the proportion of adults in England who were obese or overweight was 26% and 36%, respectively
Previous studies indicated that commonly used dietary assessment techniques, such as 24-h dietary recall (24HR), can effectively
We found that an integrated approach based on deep learning and 3-D reconstruction could be one of the potential solutions in aiding dietary assessment
Summary
A RECENT National Health Service (NHS) survey [1] disclosed that the proportion of adults in England who were obese or overweight was 26% and 36%, respectively. To estimate the portion size, it relies heavily on individuals’ subjective perception, which could be highly biased and inaccurate It is for these reasons that various objective vision-based methods, ranging from model-based [4], [5], stereo-based [6], depth camera based [7], and deep learning approaches [8], have been proposed. While these approaches present reasonable accuracy in portion size estimation, there still exist several key challenges such as view occlusion and scale ambiguity. Inspired by [9], a novel vision-based dietary assessment approach based on deep learning view synthesis and depth sensing technique is proposed in this article This approach aims to address the key problems, such as view occlusion and scale ambiguity, in volume estimation by combining the merits of artificial intelligence and depth sensing capabilities. 6) A new vision-based dietary assessment approach is developed by combining real-time 3-D reconstruction and deep learning view synthesis
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