Food Recognition Using Consensus Vocabularies

2015 ◽  
pp. 384-392 ◽  
Author(s):  
Giovanni Maria Farinella ◽  
Marco Moltisanti ◽  
Sebastiano Battiato
Keyword(s):  
Food Recognition

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.

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

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

An ensemble feature method for food classification

2017 ◽  
Vol 26 (1) ◽  
pp. 13-39
Author(s):  
Niki Martinel ◽  
Christian Micheloni ◽  
Claudio Piciarelli
Keyword(s):  
Random Forest ◽  
State Of The Art ◽  
Texture Feature ◽  
Random Forest Classifier ◽  
Texture Feature Extraction ◽  
Feature Encoding ◽  
Food Recognition ◽  
Feature Based ◽  
Food Image ◽  
Comparative Results

In the last years, several works on automatic image-based food recognition have been proposed, often based on texture feature extraction and classification. However, there is still a lack of proper comparisons to evaluate which approaches are better suited for this specific task. In this work, we adopt a Random Forest classifier to measure the performances of different texture filter banks and feature encoding techniques on three different food image datasets. Comparative results are given to show the performance of each considered approach, as well as to compare the proposed Random Forest classifiers with other feature-based state-of-the-art solutions.

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