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Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
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Single-View Food Portion Estimation Based on Geometric Models.

Shaobo Fang1, Chang Liu1, Fengqing Zhu1

  • 1Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, U.S.A.

ISM ... : ... IEEE International Symposium on Multimedia ... : Proceedings. IEEE International Symposium on Multimedia
|September 28, 2016
PubMed
Summary

This study introduces an automated technique for estimating food portion size and energy intake from a single image. The method accurately determines kilocalories consumed without manual parameter adjustments.

Keywords:
3D ReconstructionDietary AssessmentFood Portion EstimationGeometric Model

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Area of Science:

  • Computer Vision
  • Nutritional Science
  • 3D Scene Reconstruction

Background:

  • Accurate estimation of dietary intake is crucial for health management.
  • Previous methods for food portion estimation often require manual parameter tuning.
  • Single-view 3D reconstruction of food items presents significant challenges.

Purpose of the Study:

  • To develop an automated food portion estimation technique using single-view images.
  • To estimate the energy (kilocalories) consumed in a meal.
  • To overcome limitations of manual parameter tuning in previous methods.

Main Methods:

  • Utilizing geometric models of containers to aid in 3D parameter recovery of food items.
  • Determining food item volume based on estimated 3D parameters and a reference object.
  • Estimating food weight using food density and calculated volume.
  • Leveraging accurate food segmentation and classification for improved accuracy.

Main Results:

  • Achieved an energy estimation error of less than 6% for meal images.
  • Demonstrated the capability of estimating food portion without manual parameter tuning.
  • Successfully recovered 3D parameters of food items using container geometry.

Conclusions:

  • The proposed technique offers an automated and accurate method for food portion and energy estimation from single images.
  • Integration of geometric container models enhances 3D reconstruction for food analysis.
  • This approach has the potential to improve dietary assessment tools.