scholarly journals Food Recognition for Dietary Monitoring during Smoke Quitting

2021 ◽  
Author(s):  
Sebastiano Battiato ◽  
Pasquale Caponnetto ◽  
Oliver Giudice ◽  
Mazhar Hussain ◽  
Roberto Leotta ◽  
...  
Keyword(s):  
Food Recognition ◽  
Dietary Monitoring

scholarly journals A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment

Healthcare ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1676
Author(s):  
Ghalib Ahmed Tahir ◽  
Chu Kiong Loo
Keyword(s):  
Neural Networks ◽  
Dietary Habits ◽  
Volume Estimation ◽  
Estimation Methods ◽  
Monitoring Systems ◽  
Automatic Assessment ◽  
Increased Risk ◽  
Food Recognition ◽  
Food Image ◽  
Dietary Monitoring

Dietary studies showed that dietary problems such as obesity are associated with other chronic diseases, including 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, which are manageable using interactive mHealth apps. However, traditional dietary monitoring systems using manual food logging suffer from imprecision, underreporting, time consumption, and low adherence. Recent dietary monitoring systems tackle these challenges by automatic assessment of dietary intake through machine learning methods. This survey discusses the best-performing methodologies that have been developed so far for automatic food recognition and volume estimation. Firstly, the paper presented the rationale of visual-based methods for food recognition. Then, the core of the study is the presentation, discussion, and evaluation of these methods based on popular food image databases. In this context, this study discusses the mobile applications that are implementing these methods for automatic food logging. Our findings indicate that around 66.7% of surveyed studies use visual features from deep neural networks for food recognition. Similarly, all surveyed studies employed a variant of convolutional neural networks (CNN) for ingredient recognition due to recent research interest. Finally, this survey ends with a discussion of potential applications of food image analysis, existing research gaps, and open issues of this research area. Learning from unlabeled image datasets in an unsupervised manner, catastrophic forgetting during continual learning, and improving model transparency using explainable AI are potential areas of interest for future studies.

Uncertainty-Aware Data Augmentation for Food Recognition

2021 ◽  
Author(s):  
Eduardo Aguilar ◽  
Bhalaji Nagarajan ◽  
Rupali Khantun ◽  
Marc Bolanos ◽  
Petia Radeva
Keyword(s):  
Data Augmentation ◽  
Food Recognition

Food Recognition: A New Dataset, Experiments, and Results

2017 ◽  
Vol 21 (3) ◽  
pp. 588-598 ◽  
Author(s):  
Gianluigi Ciocca ◽  
Paolo Napoletano ◽  
Raimondo Schettini
Keyword(s):  
Food Recognition

scholarly journals Leveraging Context to Support Automated Food Recognition in Restaurants

2015 ◽  
Author(s):  
Vinay Bettadapura ◽  
Edison Thomaz ◽  
Aman Parnami ◽  
Gregory D. Abowd ◽  
Irfan Essa
Keyword(s):  
Food Recognition

scholarly journals Challenges and Opportunities in Robotic Food Handling: A Review

2022 ◽  
Vol 8 ◽  
Author(s):  
Zhongkui Wang ◽  
Shinichi Hirai ◽  
Sadao Kawamura
Keyword(s):  
Low Cost ◽  
Food Products ◽  
Future Research ◽  
Food Handling ◽  
Research Directions ◽  
Fundamental Information ◽  
Food Recognition ◽  
Challenges And Opportunities ◽  
Future Research Directions ◽  
End Effectors

Despite developments in robotics and automation technologies, several challenges need to be addressed to fulfill the high demand for automating various manufacturing processes in the food industry. In our opinion, these challenges can be classified as: the development of robotic end-effectors to cope with large variations of food products with high practicality and low cost, recognition of food products and materials in 3D scenario, better understanding of fundamental information of food products including food categorization and physical properties from the viewpoint of robotic handling. In this review, we first introduce the challenges in robotic food handling and then highlight the advances in robotic end-effectors, food recognition, and fundamental information of food products related to robotic food handling. Finally, future research directions and opportunities are discussed based on an analysis of the challenges and state-of-the-art developments.

A Novel SVM Based Food Recognition Method for Calorie Measurement Applications

2012 ◽  
Author(s):  
Parisa Pouladzadeh ◽  
Gregorio Villalobos ◽  
Rana Almaghrabi ◽  
Shervin Shirmohammadi
Keyword(s):  
Recognition Method ◽  
Food Recognition

Dietary Monitoring Studies on Lead and Cadmium Exposures in Poland

1991 ◽  
Vol 3 (3-4) ◽  
pp. 21-29 ◽  
Author(s):  
K. Karlowski ◽  
M. Wojciechowska-Mazurek
Keyword(s):  
Lead And Cadmium ◽  
Dietary Monitoring
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