A Review of the Vision-based Approaches for Dietary Assessment
- URL: http://arxiv.org/abs/2106.11776v1
- Date: Mon, 21 Jun 2021 06:30:06 GMT
- Title: A Review of the Vision-based Approaches for Dietary Assessment
- Authors: Ghalib Tahir and Chu Kiong Loo
- Abstract summary: Dietary-related problems such as obesity are a growing concern in todays modern world.
Computer-based food recognition offers automatic visual-based methods to assess dietary intake.
- Score: 4.347952928399708
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Dietary-related problems such as obesity are a growing concern in todays
modern world. If the current trend continues, it is most likely that the
quality of life, in general, is significantly affected since obesity is
associated with other chronic diseases such as hypertension, irregular blood
sugar levels, and increased risk of heart attacks. The primary cause of these
problems is poor lifestyle choices and unhealthy dietary habits, with emphasis
on a select few food groups such as sugars, fats, and carbohydrates. In this
regard, computer-based food recognition offers automatic visual-based methods
to assess dietary intake and help people make healthier choices. Thus, the
following paper presents a brief review of visual-based methods for food
recognition, including their accuracy, performance, and the use of popular food
databases to evaluate existing models. The work further aims to highlight
future challenges in this area. New high-quality studies for developing
standard benchmarks and using continual learning methods for food recognition
are recommended.
Related papers
- NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images [63.314702537010355]
Self-reporting methods are often inaccurate and suffer from substantial bias.
Recent work has explored using computer vision prediction systems to predict nutritional information from food images.
This paper aims to enhance the efficacy of dietary intake estimation by leveraging various neural network architectures.
arXiv Detail & Related papers (2024-05-13T14:56:55Z) - From Canteen Food to Daily Meals: Generalizing Food Recognition to More
Practical Scenarios [92.58097090916166]
We present two new benchmarks, namely DailyFood-172 and DailyFood-16, designed to curate food images from everyday meals.
These two datasets are used to evaluate the transferability of approaches from the well-curated food image domain to the everyday-life food image domain.
arXiv Detail & Related papers (2024-03-12T08:32:23Z) - Image-Based Dietary Assessment: A Healthy Eating Plate Estimation System [0.0]
The nutritional quality of diets has significantly deteriorated over the past two to three decades, a decline often underestimated by the people.
This paper introduces an innovative Image-Based Dietary Assessment system aimed at evaluating the healthiness of meals through image analysis.
Our system employs advanced image segmentation and classification techniques to analyze food items on a plate, assess their proportions, and calculate meal adherence to Harvard's healthy eating recommendations.
arXiv Detail & Related papers (2024-03-02T21:01:01Z) - NutritionVerse: Empirical Study of Various Dietary Intake Estimation Approaches [59.38343165508926]
Accurate dietary intake estimation is critical for informing policies and programs to support healthy eating.
Recent work has focused on using computer vision and machine learning to automatically estimate dietary intake from food images.
We introduce NutritionVerse- Synth, the first large-scale dataset of 84,984 synthetic 2D food images with associated dietary information.
We also collect a real image dataset, NutritionVerse-Real, containing 889 images of 251 dishes to evaluate realism.
arXiv Detail & Related papers (2023-09-14T13:29:41Z) - Food Recognition and Nutritional Apps [0.0]
Food recognition and nutritional apps are trending technologies that may revolutionise the way people with diabetes manage their diet.
These apps offer a promising solution for managing diabetes, but are rarely used by patients.
This chapter aims to provide an in-depth assessment of the current status of apps for food recognition and nutrition, to identify factors that may inhibit or facilitate their use.
arXiv Detail & Related papers (2023-06-20T13:23:59Z) - NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake
Estimation [65.47310907481042]
One in four older adults are malnourished.
Machine learning and computer vision show promise of automated nutrition tracking methods of food.
NutritionVerse-3D is a large-scale high-resolution dataset of 105 3D food models.
arXiv Detail & Related papers (2023-04-12T05:27:30Z) - Towards the Creation of a Nutrition and Food Group Based Image Database [58.429385707376554]
We propose a framework to create a nutrition and food group based image database.
We design a protocol for linking food group based food codes in the U.S. Department of Agriculture's (USDA) Food and Nutrient Database for Dietary Studies (FNDDS)
Our proposed method is used to build a nutrition and food group based image database including 16,114 food datasets.
arXiv Detail & Related papers (2022-06-05T02:41:44Z) - Vision-Based Food Analysis for Automatic Dietary Assessment [49.32348549508578]
This review presents one unified Vision-Based Dietary Assessment (VBDA) framework, which generally consists of three stages: food image analysis, volume estimation and nutrient derivation.
Deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition.
arXiv Detail & Related papers (2021-08-06T05:46:01Z) - An Intelligent Passive Food Intake Assessment System with Egocentric
Cameras [14.067860492694251]
Malnutrition is a major public health concern in low-and-middle-income countries (LMICs)
We propose to implement an intelligent passive food intake assessment system via egocentric cameras.
Our method is able to reliably monitor food intake and give feedback on users' eating behaviour.
arXiv Detail & Related papers (2021-05-07T09:47:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.