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Estimating Food Volume in a Bowl Based on Geometric Image Features

Li, Boyang (2021) Estimating Food Volume in a Bowl Based on Geometric Image Features. Master's Thesis, University of Pittsburgh. (Unpublished)

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Image-based dietary assessment is important for health monitoring and obesity management because it can provides quantitative food intake information. In this thesis, a novel image processing method that estimates the volume of food within a circular bowl (i.e., the top rim of the bowl is a circle) is presented. In contrast to the Western culture where circular plates are most commonly used as food containers, circular bowls are the primary food containers in Asian and African culture. This thesis focuses on estimating the volume of amorphous food (i.e., food without a clear shape, such as a bowl of cereal) instead of food with usual shapes (e.g., an apple). Four geometric features of the food, namely food orientation, food area ratio, normalized curvature and normalized shape vertex, are extracted from 2D images. Based on these features, food volume is estimated using a linear or quadratic regression model. Our experiments show that, for 135 images of six different foods in a bowl of known shape, the mean absolute percentage error of our estimation was less than 20\%, evaluated using a five-fold cross-validation technique.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Li, Boyangbol33@pitt.edubol330000-0002-0742-301X
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSun, Minguidrsun@pitt.edudrsun0000-0001-7948-9205
Committee CoChairMao, Zhi-Hongzhm4@pitt.eduzhm40000-0002-3025-463X
Committee MemberDallal, Ahmedahd12@pitt.eduadh120000-0003-0573-2326
Committee MemberZhan, Liangliang.zhan@pitt.eduliang.zhan0000-0002-7920-4828
Date: 13 June 2021
Date Type: Publication
Defense Date: 24 March 2021
Approval Date: 13 June 2021
Submission Date: 3 April 2021
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 53
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: food volume estimation, Hough transform, image segmentation, curvature, regression
Date Deposited: 13 Jun 2021 18:37
Last Modified: 13 Jun 2021 18:37


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