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Updated: Jul 31, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Food volume estimation by multi-layer superpixel.

Xin Zheng¹, Chenhan Liu², Yifei Gong³

¹School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China.

Mathematical Biosciences and Engineering : MBE

|May 10, 2023

Summary

This summary is machine-generated.

Accurately estimating food volume for diet monitoring is challenging. This study introduces a novel multi-layer superpixel method using stereo images and depth data for precise, automated food volume calculation.

Keywords:

disparity map food volume estimation multi-layer superpixel stereo vision

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Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

Area of Science:

Computer Vision
Nutrition Science
Biomedical Engineering

Background:

Accurate food volume estimation is crucial for diet monitoring and health management.
Existing methods often require tedious human-computer interaction and suffer from inaccuracies.
Automated and precise food volume measurement remains a significant challenge.

Purpose of the Study:

To develop an automated method for accurate food volume estimation.
To overcome limitations of existing interactive food volume estimation techniques.
To provide a convenient tool for promoting balanced diets and maintaining health.

Main Methods:

Utilized stereo camera to capture food images with depth information.
Reconstructed the plate plane and warped images/disparity maps for a parallel view.
Employed multi-layer superpixel segmentation and depth-based slicing to estimate volume.
Incorporated occlusion estimation and superpixel rescaling for enhanced accuracy.

Main Results:

The proposed method significantly reduces noise and visual errors compared to existing techniques.
Achieved improved accuracy in food volume estimation.
Demonstrated effectiveness, accuracy, and convenience through experimental validation.

Conclusions:

The novel multi-layer superpixel technique offers an effective solution for automated food volume estimation.
This method enhances accuracy by combining image data and disparity maps.
Presents a valuable new tool for dietary monitoring and health promotion.