scholarly journals Exploring Food Detection Using CNNs

2018 ◽  
pp. 339-347 ◽  
Author(s):  
Eduardo Aguilar ◽  
Marc Bolaños ◽  
Petia Radeva
Keyword(s):  
Food Detection

scholarly journals SERS Biosensor Based on Engineered 2D-Aperiodic Nanostructure for In-Situ Detection of Viable Brucella Bacterium in Complex Matrix

Nanomaterials ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 886
Author(s):  
Massimo Rippa ◽  
Riccardo Castagna ◽  
Domenico Sagnelli ◽  
Ambra Vestri ◽  
Giorgia Borriello ◽  
...  
Keyword(s):  
High Performance ◽  
Foodborne Pathogen ◽  
Surface Enhanced Raman Spectroscopy ◽  
High Performing ◽  
Sensing Platform ◽  
Food Detection ◽  
Raman Enhancement ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
In Situ Detection

Brucella is a foodborne pathogen globally affecting both the economy and healthcare. Surface Enhanced Raman Spectroscopy (SERS) nano-biosensing can be a promising strategy for its detection. We combined high-performance quasi-crystal patterned nanocavities for Raman enhancement with the use of covalently immobilized Tbilisi bacteriophages as high-performing bio-receptors. We coupled our efficient SERS nano-biosensor to a Raman system to develop an on-field phage-based bio-sensing platform capable of monitoring the target bacteria. The developed biosensor allowed us to identify Brucella abortus in milk by our portable SERS device. Upon bacterial capture from samples (104 cells), a signal related to the pathogen recognition was observed, proving the concrete applicability of our system for on-site and in-food detection.

Improving food detection for images from a wearable egocentric camera

2021 ◽  
Keyword(s):  
International Symposium ◽  
Fast Track ◽  
Electronic Imaging ◽  
Food Detection

Fast track article for IS&T International Symposium on Electronic Imaging 2021: Imaging and Multimedia Analytics in a Web and Mobile World 2021 proceedings.

Food Detection by Vultures and Condors

The Auk ◽  
1941 ◽  
Vol 58 (4) ◽  
pp. 571-571 ◽  
Author(s):  
William Vogt
Keyword(s):  
Food Detection

scholarly journals A Comparative Study of Indian Food Image Classification Using K-Nearest-Neighbour and Support-Vector-Machines

2018 ◽  
Vol 7 (3.12) ◽  
pp. 521 ◽  
Author(s):  
Pathanjali C ◽  
Vimuktha E Salis ◽  
Jalaja G ◽  
Latha A
Keyword(s):  
Comparative Study ◽  
Detection System ◽  
Experimental Result ◽  
Support Vector ◽  
Svm Classifier ◽  
Nearest Neighbour ◽  
Classification Models ◽  
Food Detection ◽  
Knn Classifier ◽  
Indian Food

Food being the vital part of everyone’s lives, food detection and recognition becomes an interesting and challenging problem in computer vision and image processing. In this paper we mainly propose an automatic food detection system that detects and recognises varieties of Indian food. This paper uses a combined colour and shape features. The K-Nearest-Neighbour (KNN) and Support-Vector -Machine (SVM) classification models are used to classify the features. A comparative study on the performance of both the classification models is performed. The experimental result shows the higher efficiency of SVM classifier over KNN classifier. 

Sign in / Sign up

Export Citation Format

Share Document