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Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

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Automatic detection of dining plates for image-based dietary evaluation.

Jie Nie1, Zhiqiang Wei, Wenyan Jia

  • 1Department of Computer Science, Ocean University of China, Qingdao, China.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
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This study introduces an automatic detector for identifying circular dining plates in images and videos, crucial for food intake and obesity research. The system demonstrates high reliability and robustness, even with complex backgrounds and occluded plates.

Area of Science:

  • Computer Vision
  • Medical Imaging
  • Obesity Research

Background:

  • Accurate measurement of food intake is essential for obesity research.
  • Automated methods are needed to analyze dietary patterns from visual data.
  • Existing methods may struggle with complex backgrounds or occluded food items.

Purpose of the Study:

  • To develop and validate an automatic detector for circular dining plates in images and videos.
  • To facilitate objective studies on food intake and obesity.
  • To create a robust system for analyzing meal content in real-world settings.

Main Methods:

  • Edge detection from input images.
  • Conversion of edges into curves using processing steps.
  • Application of arc filtering and grouping algorithms.

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Last Updated: Jun 6, 2026

Deep Neural Networks for Image-Based Dietary Assessment
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'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake
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'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake

Published on: September 18, 2018

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
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  • Identification of convex hulls and ellipse fitting for dining plate detection.

    • Main Results:

    • The developed detector accurately identifies circular dining plates.
    • The system is highly reliable in real-world image and video analysis.
    • Robust performance was observed even with complex backgrounds and severely occluded plates.

    Conclusions:

    • The automatic dining plate detector is a valuable tool for food intake and obesity studies.
    • The method offers a reliable and robust solution for analyzing visual dietary data.
    • This technology can significantly advance research in nutritional science and public health.