DepthCalorieCam: A Mobile Application for Volume-Based FoodCalorie Estimation using Depth Cameras-Reference-Cited by-同舟云学术

DepthCalorieCam: A Mobile Application for Volume-Based FoodCalorie Estimation using Depth Cameras

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management - MADiMa '19
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Author:

Ando Yoshikazu¹,Ege Takumi¹,Cho Jaehyeong¹,Yanai Keiji¹

Affiliation:

1. The University of Electro-Communications, Tokyo, Chofu, Tokyo, Japan

Funder

JSPS KAKENHI

Publisher

ACM Press

Reference17 articles.

1. D. Allegra, M. Anthimopoulos, J. Dehais, Y. Lu, F. Stanco, G. M. Farinella, and S. Mougiakakou. 2017. A Multimedia Database for Automatic Meal Assessment Systems. In Proc. of International Workshop on Multimedia Assisted Dietary Management (MADiMa).

2. L. Bossard, M. Guillaumin, and L. Van Gool. 2014. Food-101 -- Mining Discriminative Components with Random Forests. In Proc. of European Conference on Computer Vision (ECCV). 446--461.

3. L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. 2014. Semantic image segmentation with deep convolutional nets and fully connected CRFs.

4. F. Chollet. 2017. Xception: Deep Learning with Depthwise Separable Convolutions. In Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). 1800--1807.

5. J. Dehais, M. Anthimopoulos, S. Shevchik, and S. Mougiakakou. 2017. Two-View 3D Reconstruction for Food Volume Estimation. IEEE Transactions on Multimedia , Vol. 19, 5 (2017), 1090--1099.

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