Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 27, 2026

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
07:47

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

Segmentation Assisted Food Classification for Dietary Assessment.

Fengqing Zhu1, Marc Bosch, Tusarebecca Schap

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

Proceedings of Spie--The International Society for Optical Engineering
|December 1, 2011
PubMed
Summary

This study introduces a mobile app for dietary assessment, using image analysis to identify and quantify food intake. This technology aims to improve the accuracy and ease of tracking diet for better health insights.

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

QARV++: An Improved Hierarchical VAE for Learned Image Compression.

IEEE transactions on circuits and systems for video technology : a publication of the Circuits and Systems Society·2026
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

Physically Informed 3D Food Reconstruction: Methods and Results.

IEEE journal of biomedical and health informatics·2026
Same author

LncRNA HOTAIR promotes LPS-induced inflammatory responses by activating the NF-κB pathway.

Experimental biology and medicine (Maywood, N.J.)·2026
Same author

User Preferences for an Image-Assisted Dietary Recall: Qualitative Study Comparing 3 Dietary Assessment Methods.

JMIR human factors·2025
Same author

Long-Tailed Continual Learning For Visual Food Recognition.

IEEE transactions on multimedia·2025

Area of Science:

  • Nutritional Science
  • Computer Vision
  • Machine Learning

Background:

  • Accurate dietary assessment is crucial for understanding diet-health relationships.
  • Current methods can be burdensome and inaccurate.
  • Mobile device imaging shows promise for less burdensome dietary assessment.

Purpose of the Study:

  • To develop automated methods for food identification and segmentation from mobile device images.
  • To improve the accuracy of dietary assessment using computer vision and machine learning.

Main Methods:

  • Food images are segmented using Normalized Cuts based on color and intensity.
  • Color and texture features are extracted from segmented food regions.
  • Support vector machine methods are used for food classification and labeling.

More Related Videos

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake
04:46

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake

Published on: September 18, 2018

Related Experiment Videos

Last Updated: May 27, 2026

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
07:47

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake
04:46

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake

Published on: September 18, 2018

Main Results:

  • The developed methods enable automatic food item identification and segmentation from single images.
  • Refinement of segmentation based on classifier feedback improves food quantity estimation.

Conclusions:

  • Automated image analysis of food intake via mobile devices offers a promising approach for accurate and user-friendly dietary assessment.
  • This technology can enhance nutritional research and public health initiatives.