Literature DB >> 33521029

Estimating Dining Plate Size From an Egocentric Image Sequence Without a Fiducial Marker.

Wenyan Jia1, Zekun Wu1, Yiqiu Ren1, Shunxin Cao1, Zhi-Hong Mao1,2, Mingui Sun1,2,3.   

Abstract

Despite the extreme importance of food intake in human health, it is currently difficult to conduct an objective dietary assessment without individuals' self-report. In recent years, a passive method utilizing a wearable electronic device has emerged. This device acquires food images automatically during the eating process. These images are then analyzed to estimate intakes of calories and nutrients, assisted by advanced computational algorithms. Although this passive method is highly desirable, it has been thwarted by the requirement of a fiducial marker which must be present in the image for a scale reference. The importance of this scale reference is analogous to the importance of the scale bar in a map which determines distances or areas in any geological region covered by the map. Likewise, the sizes or volumes of arbitrary foods on a dining table covered by an image cannot be determined without the scale reference. Currently, the fiducial marker (often a checkerboard card) serves as the scale reference which must be present on the table before taking pictures, requiring human efforts to carry, place and retrieve the fiducial marker manually. In this work, we demonstrate that the fiducial marker can be eliminated if an individual's dining location is fixed and a one-time calibration using a circular plate of known size is performed. When the individual uses another circular plate of an unknown size, our algorithm estimates its radius using the range of pre-calibrated distances between the camera and the plate from which the desired scale reference is determined automatically. Our comparative experiment indicates that the mean absolute percentage error of the proposed estimation method is ~10.73%. Although this error is larger than that of the manual method of 6.68% using a fiducial marker on the table, the new method has a distinctive advantage of eliminating the manual procedure and automatically generating the scale reference.
Copyright © 2021 Jia, Wu, Ren, Cao, Mao and Sun.

Entities:  

Keywords:  dining plate size; egocentric image; fiducial marker; technology-based dietary assessment; wearable device

Year:  2021        PMID: 33521029      PMCID: PMC7840562          DOI: 10.3389/fnut.2020.519444

Source DB:  PubMed          Journal:  Front Nutr        ISSN: 2296-861X


  11 in total

1.  Automatic detection of dining plates for image-based dietary evaluation.

Authors:  Jie Nie; Zhiqiang Wei; Wenyan Jia; Lu Li; John D Fernstrom; Robert J Sclabassi; Mingui Sun
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2010

2.  Automatic food detection in egocentric images using artificial intelligence technology.

Authors:  Wenyan Jia; Yuecheng Li; Ruowei Qu; Thomas Baranowski; Lora E Burke; Hong Zhang; Yicheng Bai; Juliet M Mancino; Guizhi Xu; Zhi-Hong Mao; Mingui Sun
Journal:  Public Health Nutr       Date:  2018-03-26       Impact factor: 4.022

Review 3.  Image-assisted dietary assessment: a systematic review of the evidence.

Authors:  Luke Gemming; Jennifer Utter; Cliona Ni Mhurchu
Journal:  J Acad Nutr Diet       Date:  2014-11-11       Impact factor: 4.910

4.  An exploratory study on a chest-worn computer for evaluation of diet, physical activity and lifestyle.

Authors:  Mingui Sun; Lora E Burke; Thomas Baranowski; John D Fernstrom; Hong Zhang; Hsin-Chen Chen; Yicheng Bai; Yuecheng Li; Chengliu Li; Yaofeng Yue; Zhen Li; Jie Nie; Robert J Sclabassi; Zhi-Hong Mao; Wenyan Jia
Journal:  J Healthc Eng       Date:  2015       Impact factor: 2.682

5.  Imaged based estimation of food volume using circular referents in dietary assessment.

Authors:  Wenyan Jia; Yaofeng Yue; John D Fernstrom; Ning Yao; Robert J Sclabassi; Madelyn H Fernstrom; Mingui Sun
Journal:  J Food Eng       Date:  2011-10-06       Impact factor: 5.354

6.  Use of technology in children's dietary assessment.

Authors:  C J Boushey; D A Kerr; J Wright; K D Lutes; D S Ebert; E J Delp
Journal:  Eur J Clin Nutr       Date:  2009-02       Impact factor: 4.016

7.  Food Volume Estimation Based on Deep Learning View Synthesis from a Single Depth Map.

Authors:  Frank P-W Lo; Yingnan Sun; Jianing Qiu; Benny Lo
Journal:  Nutrients       Date:  2018-12-18       Impact factor: 5.717

8.  Health effects of dietary risks in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017.

Authors: 
Journal:  Lancet       Date:  2019-04-04       Impact factor: 79.321

9.  Evaluation of a smartphone food diary application using objectively measured energy expenditure.

Authors:  Felicity J Pendergast; Nicola D Ridgers; Anthony Worsley; Sarah A McNaughton
Journal:  Int J Behav Nutr Phys Act       Date:  2017-03-14       Impact factor: 6.457

10.  A Novel Mobile Structured Light System in Food 3D Reconstruction and Volume Estimation.

Authors:  Sepehr Makhsous; Hashem M Mohammad; Jeannette M Schenk; Alexander V Mamishev; Alan R Kristal
Journal:  Sensors (Basel)       Date:  2019-01-29       Impact factor: 3.576

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