777 research outputs found
A Multi-Task Learning Approach for Meal Assessment
Key role in the prevention of diet-related chronic diseases plays the
balanced nutrition together with a proper diet. The conventional dietary
assessment methods are time-consuming, expensive and prone to errors. New
technology-based methods that provide reliable and convenient dietary
assessment, have emerged during the last decade. The advances in the field of
computer vision permitted the use of meal image to assess the nutrient content
usually through three steps: food segmentation, recognition and volume
estimation. In this paper, we propose a use one RGB meal image as input to a
multi-task learning based Convolutional Neural Network (CNN). The proposed
approach achieved outstanding performance, while a comparison with
state-of-the-art methods indicated that the proposed approach exhibits clear
advantage in accuracy, along with a massive reduction of processing time
Food Recognition and Nutritional Apps
Food recognition and nutritional apps are trending technologies that may
revolutionise the way people with diabetes manage their diet. Such apps can
monitor food intake as a digital diary and even employ artificial intelligence
to assess the diet automatically. Although these apps offer a promising
solution for managing diabetes, they 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, while it is accompanied by an outline of relevant research and
development.Comment: This book chapter: Food Recognition and Nutritional Apps is set to
appear in the book: "Diabetes Digital Health, Telehealth, and Artificial
Intelligence
Narrative Review: Food Image Use for Machine Learnings’ Function in Dietary Assessment and Real Time Nutrition Feedback and Education
Technology has played a key role in advancing the health and agriculture sectors to improve obesity rates, diseasecontrol, food waste, and overall health disparities. However, these health and lifestyle determinants continue to plague theUnited States population. While new technologies have been and are currently being developed to address these concerns, they may not be practical for the general population. Utilizing machine learning advancement in food recognition using smartphone technology may be a means to improve the dietary component of nutrition assessments while providing valuable nutrition feedback. This narrative review was conducted to assess the current state of the literature on nutrition technology using image recognition for practical applications, while also proposing theoretical uses for the technology to improve quality of life through dietary feedback
Food Object Recognition Using a Mobile Device: State of the Art
In this paper nine mobile food recognition systems are described based on their system architecture and their core properties (the core properties and experimental results are shown on the last page). While the mobile hardware increased its power through the years (2009 - 2013) and the food detection algorithms got optimized, still there was no uniform approach to the question of food detection. Also, some system used additional information for better detection, like voice data, OCR and bounding boxes. Three systems included a volume estimation feature. First five systems were implemented on a client-server architecture, while the last three took advantage of the available hardware in later years and proposed a client only based architecture
Food Object Recognition Using a Mobile Device: State of the Art
In this paper nine mobile food recognition systems are described based on their system architecture and their core properties (the core properties and experimental results are shown on the last page). While the mobile hardware increased its power through the years (2009 - 2013) and the food detection algorithms got optimized, still there was no uniform approach to the question of food detection. Also, some system used additional information for better detection, like voice data, OCR and bounding boxes. Three systems included a volume estimation feature. First five systems were implemented on a client-server architecture, while the last three took advantage of the available hardware in later years and proposed a client only based architecture
Dietary assessment and obesity aviodance system based on vision: A review
Using technology for food objects recognition and estimation of its calories is very useful to spread food culture and awareness among people in the age of obesity due to the bad habits of food consumption and wide range of inappropriate food products.Image based sensing of such system
is very promising with the large expanding of camera embedded portable devices such as smartphones, PC tablets, and laptops.In the past decade, researchers have been working on developing a reliable image based system for food recognition and calories estimation.Different approaches have tackled the system from different aspects.This paper reviews the state of the
art of this interesting application, and presents its experimental results.Future work of research is presented in order to guide new researchers toward potential tracks to create more maturity and reliability to this application
Partially Supervised Multi-Task Network for Single-View Dietary Assessment
Food volume estimation is an essential step in the pipeline of dietary
assessment and demands the precise depth estimation of the food surface and
table plane. Existing methods based on computer vision require either
multi-image input or additional depth maps, reducing convenience of
implementation and practical significance. Despite the recent advances in
unsupervised depth estimation from a single image, the achieved performance in
the case of large texture-less areas needs to be improved. In this paper, we
propose a network architecture that jointly performs geometric understanding
(i.e., depth prediction and 3D plane estimation) and semantic prediction on a
single food image, enabling a robust and accurate food volume estimation
regardless of the texture characteristics of the target plane. For the training
of the network, only monocular videos with semantic ground truth are required,
while the depth map and 3D plane ground truth are no longer needed.
Experimental results on two separate food image databases demonstrate that our
method performs robustly on texture-less scenarios and is superior to
unsupervised networks and structure from motion based approaches, while it
achieves comparable performance to fully-supervised methods
Image-based food classification and volume estimation for dietary assessment: a review.
A daily dietary assessment method named 24-hour dietary recall has commonly been used in nutritional epidemiology studies to capture detailed information of the food eaten by the participants to help understand their dietary behaviour. However, in this self-reporting technique, the food types and the portion size reported highly depends on users' subjective judgement which may lead to a biased and inaccurate dietary analysis result. As a result, a variety of visual-based dietary assessment approaches have been proposed recently. While these methods show promises in tackling issues in nutritional epidemiology studies, several challenges and forthcoming opportunities, as detailed in this study, still exist. This study provides an overview of computing algorithms, mathematical models and methodologies used in the field of image-based dietary assessment. It also provides a comprehensive comparison of the state of the art approaches in food recognition and volume/weight estimation in terms of their processing speed, model accuracy, efficiency and constraints. It will be followed by a discussion on deep learning method and its efficacy in dietary assessment. After a comprehensive exploration, we found that integrated dietary assessment systems combining with different approaches could be the potential solution to tackling the challenges in accurate dietary intake assessment
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