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MODEL-BASED FOOD VOLUME ESTIMATION USING 3D POSE.

Chang Xu1, Ye He1, Nitin Khanna2

  • 1School of Electrical and Computer Engineering, Purdue University.

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|June 3, 2017
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Summary
This summary is machine-generated.

This study introduces a new method for estimating food portion sizes from single meal images. The approach uses 3D models and image analysis to accurately determine food volume for dietary assessment.

Keywords:
3D model rendering3D reconstructiondietary assessmentimage segmentationpose estimation

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Area of Science:

  • Computer Vision
  • Dietary Assessment
  • Food Science

Background:

  • Accurate food volume estimation is crucial for dietary assessment systems that analyze mobile device images.
  • Current methods may lack precision in quantifying food portions from visual data.

Purpose of the Study:

  • To develop and validate a novel method for estimating the portion size of rigid food items from a single image.
  • To improve the accuracy of automated dietary assessment systems.

Main Methods:

  • Developed a food portion size estimation method using 3D graphical models created via multi-view 3D reconstruction.
  • Utilized camera calibration to determine food item pose (translation, elevation) relative to the camera.
  • Employed image similarity measures to refine the final pose estimation.

Main Results:

  • The proposed model-based approach demonstrated accuracy and reliability in estimating food volume.
  • Experimental validation was conducted across four distinct food categories.

Conclusions:

  • The novel method effectively estimates food portion sizes from single images.
  • This approach enhances the capabilities of automated dietary assessment systems for nutrient content determination.