research-article

Food Volume Estimation Based on Reference

Authors:

Yongquan YangAuthors Info & Claims

ICIAI '20: Proceedings of the 2020 the 4th International Conference on Innovation in Artificial Intelligence

Pages 84 - 89

https://doi.org/10.1145/3390557.3394123

Published: 04 June 2020 Publication History

Abstract

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.

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Cited By

Lee DKwon S(2024)Amount Estimation Method for Food Intake Based on Color and Depth Images through Deep LearningSensors10.3390/s2407204424:7(2044)Online publication date: 22-Mar-2024
https://doi.org/10.3390/s24072044
Zhang JXu YZhang HLi H(2024)Fine-Grained Food Image Segmentation Method Based on MS-Mask2Former2024 International Conference on Artificial Intelligence of Things and Systems (AIoTSys)10.1109/AIoTSys63104.2024.10780517(1-8)Online publication date: 17-Oct-2024
https://doi.org/10.1109/AIoTSys63104.2024.10780517
Kim JLee DKwon S(2023)Food Classification and Meal Intake Amount Estimation through Deep LearningApplied Sciences10.3390/app1309574213:9(5742)Online publication date: 6-May-2023
https://doi.org/10.3390/app13095742
Show More Cited By

Index Terms

Food Volume Estimation Based on Reference
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding

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cover image ACM Other conferences

ICIAI '20: Proceedings of the 2020 the 4th International Conference on Innovation in Artificial Intelligence

May 2020

271 pages

ISBN:9781450376587

DOI:10.1145/3390557

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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The Hong Kong Polytechnic: The Hong Kong Polytechnic University
Xi'an Jiaotong-Liverpool University: Xi'an Jiaotong-Liverpool University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 June 2020

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The Chinese Center For Disease Control And Prevention

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ICIAI 2020

ICIAI 2020: 2020 the 4th International Conference on Innovation in Artificial Intelligence

May 8 - 11, 2020

Xiamen, China

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Cited By

Lee DKwon S(2024)Amount Estimation Method for Food Intake Based on Color and Depth Images through Deep LearningSensors10.3390/s2407204424:7(2044)Online publication date: 22-Mar-2024
https://doi.org/10.3390/s24072044
Zhang JXu YZhang HLi H(2024)Fine-Grained Food Image Segmentation Method Based on MS-Mask2Former2024 International Conference on Artificial Intelligence of Things and Systems (AIoTSys)10.1109/AIoTSys63104.2024.10780517(1-8)Online publication date: 17-Oct-2024
https://doi.org/10.1109/AIoTSys63104.2024.10780517
Kim JLee DKwon S(2023)Food Classification and Meal Intake Amount Estimation through Deep LearningApplied Sciences10.3390/app1309574213:9(5742)Online publication date: 6-May-2023
https://doi.org/10.3390/app13095742
Konstantakopoulos FGeorga EFotiadis D(2023)A novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithmsScientific Reports10.1038/s41598-023-47885-013:1Online publication date: 29-Nov-2023
https://doi.org/10.1038/s41598-023-47885-0
Jia WRen YLi BBeatrice BQue JCao SWu ZMao ZLo BAnderson AFrost GMcCrory MSazonov ESteiner-Asiedu MBaranowski TBurke LSun M(2022)A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume EstimationSensors10.3390/s2204149322:4(1493)Online publication date: 15-Feb-2022
https://doi.org/10.3390/s22041493
Smith SAdam MManning GBurrows TCollins CRollo M(2022)Food Volume Estimation by Integrating 3D Image Projection and Manual Wire Mesh TransformationsIEEE Access10.1109/ACCESS.2022.317158410(48367-48378)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3171584

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