Novel non linear PD controller for the positioning system of a 2D Automated Visual Inspection Machine for Quality Control of SMD

2006 ◽  
Author(s):  
Noe Perez-Arreortua ◽  
Francisco J. Ruiz-Sanchez ◽  
Nicolas A. Plata-Ramirez
Keyword(s):  
Quality Control ◽  
Visual Inspection ◽  
Positioning System ◽  
Pd Controller ◽  
Automated Visual Inspection ◽  
Non Linear ◽  
Inspection Machine

Automated visual inspection stations for next-generation semiconductor package quality control

1996 ◽  
Author(s):  
Mark R. DeYong ◽  
Thomas C. Eskridge ◽  
John W. Grace ◽  
Jeff E. Newberry ◽  
J. H. Jones ◽  
...  
Keyword(s):  
Quality Control ◽  
Visual Inspection ◽  
Next Generation ◽  
Automated Visual Inspection

Automatic Visual Inspection Machine for Pharmaceutical Infusion Bags Implementing Cellular Neural Networks

2021 ◽  
Author(s):  
Francesco Marrone ◽  
Gianluca Zoppo ◽  
Luca Vescovi ◽  
Filippo Begarani ◽  
Ada Palama ◽  
...  
Keyword(s):  
Neural Networks ◽  
Visual Inspection ◽  
Cellular Neural Networks ◽  
Automatic Visual Inspection ◽  
Inspection Machine

scholarly journals Automated visual inspection of an airplane exterior

2015 ◽  
Author(s):  
Igor Jovančević ◽  
Jean-José Orteu ◽  
Thierry Sentenac ◽  
Rémi Gilblas
Keyword(s):  
Visual Inspection ◽  
Automated Visual Inspection

scholarly journals Analysis of texture features for wood defect classification

2020 ◽  
Vol 9 (1) ◽  
pp. 121-128
Author(s):  
Nur Dalila Abdullah ◽  
Ummi Raba'ah Hashim ◽  
Sabrina Ahmad ◽  
Lizawati Salahuddin
Keyword(s):  
Classification Accuracy ◽  
Visual Inspection ◽  
Visual Analysis ◽  
Computational Cost ◽  
Texture Features ◽  
Feature Analysis ◽  
Defect Classification ◽  
Grey Level ◽  
Automated Visual Inspection ◽  
Accuracy Measures

Selecting important features in classifying wood defects remains a challenging issue to the automated visual inspection domain. This study aims to address the extraction and analysis of features based on statistical texture on images of wood defects. A series of procedures including feature extraction using the Grey Level Dependence Matrix (GLDM) and feature analysis were executed in order to investigate the appropriate displacement and quantisation parameters that could significantly classify wood defects. Samples were taken from the KembangSemangkuk (KSK), Meranti and Merbau wood species. Findings from visual analysis and classification accuracy measures suggest that the feature set with the displacement parameter, d=2, and quantisation level, q=128, shows the highest classification accuracy. However, to achieve less computational cost, the feature set with quantisation level, q=32, shows acceptable performance in terms of classification accuracy.

Progress toward an automated visual inspection system

1990 ◽  
Author(s):  
P. COLEMAN ◽  
S. NELSON ◽  
J. MARAM ◽  
A. NORMAN
Keyword(s):  
Visual Inspection ◽  
Inspection System ◽  
Automated Visual Inspection
Sign in / Sign up

Export Citation Format

Share Document