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Deep Neural Networks for Image-Based Dietary Assessment
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Image-Based Food Volume Estimation.

Xu Chang1, He Ye1, Parra Albert1

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

CEA'13 : Proceedings of the 5Th International Workshop on Multimedia for Cooking & Eating Activities : October 21, 2013, Barcelona, Spain. Workshop on Multimedia for Cooking and Eating Activities (5Th : 2013 : Barcelona, Spain)
|June 3, 2017
PubMed
Summary
This summary is machine-generated.

This study enhances food portion size estimation using single-view and multi-view methods. The techniques leverage prior food identification data and "Shape from Silhouettes" for accurate and reliable volume measurement.

Keywords:
3D reconstructiondietary assessmentmobile applicationportion estimationpose estimation

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Area of Science:

  • Computer Vision
  • Food Science
  • Nutritional Informatics

Background:

  • Accurate food portion size estimation is crucial for dietary assessment and health management.
  • Previous work established single-view food volume estimation methods.
  • Multi-view approaches offer potential for improved accuracy in volume measurement.

Purpose of the Study:

  • To extend previous food portion size estimation techniques.
  • To develop and evaluate a multi-view volume estimation method for food.
  • To improve the accuracy and reliability of food volume measurements.

Main Methods:

  • Single-view food volume estimation utilizing segmentation and food labels from prior identification.
  • Multi-view food volume estimation employing the "Shape from Silhouettes" technique.
  • Experimental validation of developed volume estimation methodologies.

Main Results:

  • The proposed single-view method integrates prior segmentation and labeling for volume calculation.
  • The multi-view method effectively uses "Shape from Silhouettes" for portion size estimation.
  • Experimental results confirm the accuracy and reliability of both estimation techniques.

Conclusions:

  • The extended methods provide accurate and reliable food portion size estimation.
  • The integration of single-view and multi-view approaches offers a robust solution.
  • This work contributes to advancements in automated dietary assessment tools.