Evidence map›Paper›PMID 39338803›Full record

ArticleSensors (Basel, Switzerland)2024

Dining Bowl Modeling and Optimization for Single-Image-Based Dietary Assessment.

Boyang Li, Mingui Sun, Zhi-Hong Mao, Wenyan Jia

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Boyang LiDepartment of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Mingui SunDepartment of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA.ORCID 0000-0001-7948-9205
Zhi-Hong MaoDepartment of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Wenyan JiaDepartment of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA.

Funding

A Human-Mimetic AI System for Automatic, Passive and Objective Dietary AssessmentR01DK127310 · NIDDK · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SUN, MINGUI · 2021 to 2024
$2.5M
Automatic Recognition of Diet, Physical Activity and Sedentary Behavior Using a Smart Wearable DeviceR56DK113819 · NIDDK · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SUN, MINGUI · 2018 to 2018
$490k
NIDDK NIH HHS R01 DK127310NIDDK NIH HHS R56 DK113819NIH HHS 1R01DK127310-04A1
6 · The paper itself

Abstract

In dietary assessment using a single-view food image, an object of known size, such as a checkerboard, is often placed manually in the camera's view as a scale reference to estimate food volume. This traditional scale reference is inconvenient to use because of the manual placement requirement. Consequently, utensils, such as plates and bowls, have been suggested as alternative references. Although these references do not need a manual placement procedure, there is a unique challenge when a dining bowl is used as a reference. Unlike a dining plate, whose shallow shape does not usually block the view of the food, a dining bowl does obscure the food view, and its shape may not be fully observable from the single-view food image. As a result, significant errors may occur in food volume estimation due to the unknown shape of the bowl. To address this challenge, we present a novel method to premeasure both the size and shape of the empty bowl before it is used in a dietary assessment study. In our method, an image is taken with a labeled paper ruler adhered to the interior surface of the bowl, a mathematical model is developed to describe its shape and size, and then an optimization method is used to determine the bowl parameters based on the locations of observed ruler makers from the bowl image. Experimental studies were performed using both simulated and actual bowls to assess the reliability and accuracy of our bowl measurement method.

Indexed as

Image Processing, Computer-AssistedAlgorithmsDietFoodHumansModels, Theoretical3D reconstructionbowl modelingimage-based dietary assessmentoptimization

Identifiers

PMID39338803
PMCPMC11435675

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.