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MUSEFood: Multi-sensor-based Food Volume Estimation on Smartphones by Gao, Junyi; Tan, Weihao; Ma, Liantao; Wang, Yasha; Tang, Wen is a scholarly article available to read on EtoBox.

What is MUSEFood: Multi-sensor-based Food Volume Estimation on Smartphones about?

Researches have shown that diet recording can help people increase awareness of food intake and improve nutrition management, and thereby maintain a healthier life. Recently, researchers have been working on smartphone-based diet recording methods and applications that help users accomplish two tasks: record what they eat and how much they eat. Although the former task has made great progress through adopting image recognition technology, it is still a challenge to estimate the volume of foods accurately and conveniently. In this paper, we propose a novel method, named MUSEFood, for food volume estimation. MUSEFood uses the camera to capture photos of the food, but unlike existing volume measurement methods, MUSEFood requires neither training images with volume information nor placing a reference object of known size while taking photos. In addition, considering the impact of different containers on the contour shape of foods, MUSEFood uses a multi-task learning framework to improve the accuracy of food segmentation, and uses a differential model applicable for various containers to further reduce the negative impact of container differences on volume estimation accuracy. Furthermo

Author

Gao, Junyi; Tan, Weihao; Ma, Liantao; Wang, Yasha; Tang, Wen

Published

2019

Language

EN