Image-Based Estimation of Real Food Size for Accurate Food Calorie Estimation

被引：22

作者：

Ege, Takumi ^{[1
]}

Ando, Yoshikazu ^{[1
]}

Tanno, Ryosuke ^{[1
]}

Shimoda, Wataru ^{[1
]}

Yanai, Keiji ^{[1
]}

机构：

[1] Univ Electrocommun, Dept Informat, Tokyo 1-5-1 Chofugaoka, Chofu, Tokyo 1828585, Japan

来源：

2019 2ND IEEE CONFERENCE ON MULTIMEDIA INFORMATION PROCESSING AND RETRIEVAL (MIPR 2019) | 2019年

关键词：

D O I：

10.1109/MIPR.2019.00056

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, we review our works on image-based estimation of real size of foods for accurate food calorie estimation which including three existing works and two new works: (1) "CalorieCam" which is a system to estimate real food size based on a reference object, (2) Region segmentation based food calorie estimation, (3) "AR DeepCalorieCam V2" which is based on visual inertial odometry built in the iOS ARKit library, (4) "DepthCalorieCam" which employs stereo cameras on iPhone X/XS, and (5) "RiceCalorieCam" which exploits rice grains as reference objects. Especially, the last two new methods achieved 10% or less estimation error, which was enough for robust food calorie estimation.

引用

页码：274 / 279

页数：6

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