Using distance estimation and deep learning to simplify calibration in food calorie measurement

2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2015-02-11 DOI:10.1109/CIVEMSA.2015.7158594

Pallavi Kuhad, A. Yassine, S. Shirmohammadi

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引用次数: 35

Abstract

High calorie intake in the human body on the one hand, has proved harmful in numerous occasions leading to several diseases and on the other hand, a standard amount of calorie intake has been deemed essential by dietitians to maintain the right balance of calorie content in human body. As such, researchers have proposed a variety of automatic tools and systems to assist users measure their calorie in-take. In this paper, we consider the category of those tools that use image processing to recognize the food, and we propose a method for fully automatic and user-friendly calibration of the dimension of the food portion sizes, which is needed in order to measure food portion weight and its ensuing amount of calories. Experimental results show that our method, which uses deep learning, mobile cloud computing, distance estimation and size calibration inside a mobile device, leads to an accuracy improvement to 95 percent on average compared to previous work.

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利用距离估计和深度学习简化食物卡路里测量的校准

一方面，人体摄入的高热量在许多情况下被证明是有害的，会导致几种疾病;另一方面，标准的卡路里摄入量被营养师认为是保持人体卡路里含量平衡的必要条件。因此，研究人员提出了各种自动工具和系统来帮助用户测量他们的卡路里摄入量。在本文中，我们考虑了那些使用图像处理来识别食物的工具的类别，我们提出了一种全自动和用户友好的校准食物份量尺寸的方法，这是为了测量食物份量重量及其随后的卡路里量所需要的。实验结果表明，我们的方法使用了深度学习、移动云计算、距离估计和移动设备内部的尺寸校准，与以前的工作相比，平均精度提高到95%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)

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