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Experimental Manipulation of Body Size to Estimate Morphological Scaling Relationships in Drosophila
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A COMPARISON OF FOOD PORTION SIZE ESTIMATION USING GEOMETRIC MODELS AND DEPTH IMAGES.

Shaobo Fang1, Fengqing Zhu1, Chufan Jiang2

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

Proceedings. International Conference on Image Processing
|November 13, 2018
PubMed
Summary

Accurate dietary intake measurement is crucial for preventing chronic diseases. This study found 3D geometric models more accurately estimate food portion size from images than depth images.

Keywords:
3D ReconstructionDepth ImageFood Portion EstimationGeometric ModelStructured Light

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Area of Science:

  • Nutrition Science
  • Computer Vision
  • Health Informatics

Background:

  • Diet is linked to six of the top ten leading causes of death in the US, including cancer, diabetes, and heart disease.
  • Accurate dietary intake assessment is vital for developing effective disease prevention strategies.
  • Measuring food portion size from images is a challenging but important aspect of dietary assessment.

Purpose of the Study:

  • To compare the accuracy of two techniques for estimating food portion size from images: 3D geometric models and depth images.
  • To develop a method for detecting the reference plane in depth images for portion size estimation.

Main Methods:

  • Developed an expectation-maximization algorithm to detect reference planes in depth images.
  • Compared volume estimation accuracy using 3D geometric models versus depth image analysis.
  • Utilized images of food for portion size estimation.

Main Results:

  • 3D geometric models provided more accurate volume estimations for objects with well-defined shapes.
  • Depth image-based estimation showed limitations compared to geometric models for certain food items.
  • The developed expectation-maximization technique aids in depth image-based portion size estimation.

Conclusions:

  • 3D geometric modeling is a more reliable method for estimating food portion size from images, especially for foods with clear 3D structures.
  • Further research is needed to improve depth image-based portion size estimation techniques.
  • Accurate portion size estimation from food images can significantly aid in dietary intake assessment and chronic disease prevention efforts.