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Food Volume Estimation Based on Reference by Liu, Yuanyuan ;Lai, Jianqiang ;Sun, Wenjie ;Wei, Zhiqiang ;Liu, Ailing ;Gong, Weiyan ;Yang, Yongquan is a scholarly article available to read on EtoBox.

What is Food Volume Estimation Based on Reference about?

Accurate estimation of food volume is critical in the medical field. However, estimating food volume is a challenging task due to the diverse nature of food, multi-scale and other characteristics. In this paper, we explore the relationship between the properties and volume of the object (food and reference) in the image. By combining Faster R-CNN, Grabcut, Median filtering, and CNN algorithm, we propose a framework for estimating food volume based on reference. The framework uses a front view which contains reference and food to estimate food volume and is applied to image datasets for 5 kinds of foods. The experimental results show the effective performance of this method for predicting volume, and the mean absolute error of each kind of food is less than 4.5%, which shows the model is robust to estimate volume for irregular food. ## CCS Concepts Computing methodologies➝Artificial intelligence➝Computer vision ➝ Computer vision tasks ➝ Activity recognition and understanding.

Author

Liu, Yuanyuan ;Lai, Jianqiang ;Sun, Wenjie ;Wei, Zhiqiang ;Liu, Ailing ;Gong, Weiyan ;Yang, Yongquan

Publisher

ACM

Published

2020

Language

DA