Honey characterization using computer vision system and artificial neural networks

2014 ◽  
Vol 159 ◽  
pp. 143-150 ◽  
Author(s):  
Sahameh Shafiee ◽  
Saeid Minaei ◽  
Nasrollah Moghaddam-Charkari ◽  
Mohsen Barzegar
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Artificial Neural Networks ◽  
Vision System ◽  
Computer Vision System ◽  
Artificial Neural

A classification system for beans using computer vision system and artificial neural networks

2007 ◽  
Vol 78 (3) ◽  
pp. 897-904 ◽  
Author(s):  
Kıvanç Kılıç ◽  
İsmail Hakki Boyacı ◽  
Hamit Köksel ◽  
İsmail Küsmenoğlu
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Artificial Neural Networks ◽  
Classification System ◽  
Vision System ◽  
Computer Vision System ◽  
Artificial Neural

scholarly journals Applicationof Computer Visionfor Polishing RobotinAutomotive Manufacturing Industries

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
Adnan Rachmat Anom Besari ◽  
Ruzaidi Zamri ◽  
Md. Dan Md. Palil ◽  
Anton Satria Prabuwono
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Networks ◽  
Surface Defects ◽  
Vision System ◽  
Real Data ◽  
Surface Measurement ◽  
Vision Sensor ◽  
Skilled Workers ◽  
Artificial Neural

Polishing is a highly skilled manufacturing process with a lot of constraints and interaction with environment. In general, the purpose of polishing is to get the uniform surface roughness distributed evenly throughout part’s surface. In order to reduce the polishing time and cope with the shortage of skilled workers, robotic polishing technology has been investigated. This paper studies about vision system to measure surface defects that have been characterized to some level of surface roughness. The surface defects data have learned using artificial neural networks to give a decision in order to move the actuator of arm robot. Force and rotation time have chosen as output parameters of artificial neural networks. Results shows that although there is a considerable change in both parameter values acquired from vision data compared to real data, it is still possible to obtain surface defects characterization using vision sensor to a certain limit of accuracy. The overall results of this research would encourage further developments in this area to achieve robust computer vision based surface measurement systems for industrial robotic, especially in polishing process.Keywords: polishing robot, vision sensor, surface defects, and artificial neural networks

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