Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: May 25, 2026

Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

Published on: March 19, 2021

Automatic portion estimation and visual refinement in mobile dietary assessment.

Insoo Woo¹, Karl Otsmo, Sungye Kim

¹School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana USA.

Proceedings of Spie--The International Society for Optical Engineering

|January 14, 2012

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

TransVort: A Temporally-Coherent Physics-Guided Neural Network for Super-Resolving and Denoising 4D Flow MRI of Cerebrospinal Fluid.

IEEE transactions on bio-medical engineering·2026

Same author

Application of an Ecological Approach to Assessment of Nutrition in School-Aged Children-A Report from the "Biomarkers of Nutrition for Development: Knowledge Indicating Dietary Sufficiency (BOND-KIDS)" Project Working Group 3.

The Journal of nutrition·2025

Same author

Distributed and networked analysis of volumetric image data for remote collaboration of microscopy image analysis.

Journal of medical imaging (Bellingham, Wash.)·2025

Same author

The association of cognitive task scores with energy intake measurement error from technology-assisted 24-h recalls.

The British journal of nutrition·2025

Same author

Diet Quality and Eating Frequency Were Associated with Insulin-Taking Status among Adults.

Nutrients·2024

Same author

Super-Resolving and Denoising 4D flow MRI of Neurofluids Using Physics-Guided Neural Networks.

Annals of biomedical engineering·2024

Same journal

AVA: Automated Viewability Analysis for Ureteroscopic Intrarenal Surgery.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same journal

Kidney Endoscopy Video to Preoperative CT Alignment for Depth Estimation.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same journal

Deep learning‑based cell type prediction in lung tissue from brightfield histology using CODEX-derived labels.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same journal

Reconstructing physiological signals from fMRI across the adult lifespan.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same journal

Axially Swept Light-Sheet Microscopy using scattering and fluorescence contrast mechanisms.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same journal

Analytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts.

Proceedings of SPIE--the International Society for Optical Engineering·2026

See all related articles

Accurately estimating food portion size from photos is key for automated dietary assessment. This study presents a novel method using image analysis to calculate food volumes for precise nutrient intake monitoring.

Area of Science:

Nutritional science
Computer vision
Image processing

Background:

Growing concern for obesity necessitates accurate dietary intake monitoring.
Mobile devices offer a potential platform for automated dietary assessment through meal photography.

Purpose of the Study:

To develop and evaluate a method for automatic estimation of food portion size from meal images.
To enable accurate nutrient content extrapolation based on estimated food volumes.

Main Methods:

Utilizing camera parameter estimation and 3D model reconstruction to determine food volumes from images.
Developing algorithms for automatic portion size calculation from digital photographs of meals.

Main Results:

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Published on: February 19, 2021

Related Experiment Videos

Last Updated: May 25, 2026

Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

Published on: March 19, 2021

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Published on: February 19, 2021

Demonstrated the potential of the image-based approach for portion volume estimation.

Presented initial accuracy evaluation results using both real and simulated meal images.

Conclusions:

The proposed method shows promise for automated dietary assessment by accurately estimating food portion sizes from images.
This technology can aid in managing dietary imbalance and obesity concerns.