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

scholarly journals Applying Ancestry and Sex Computation as a Quality Control Tool in Targeted Next-Generation Sequencing

2016 ◽  
Vol 145 (3) ◽  
pp. 308-315 ◽  
Author(s):  
Patrick C. Mathias ◽  
Emily H. Turner ◽  
Sheena M. Scroggins ◽  
Stephen J. Salipante ◽  
Noah G. Hoffman ◽  
...  
Keyword(s):  
Quality Control ◽  
Next Generation Sequencing ◽  
Next Generation ◽  
Targeted Next Generation Sequencing ◽  
Quality Control Tool ◽  
Generation Sequencing ◽  
Control Tool

scholarly journals NGS-QCbox and Raspberry for Parallel, Automated and Rapid Quality Control Analysis of Large-Scale Next Generation Sequencing (Illumina) Data

PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0139868 ◽  
Author(s):  
Mohan A. V. S. K. Katta ◽  
Aamir W. Khan ◽  
Dadakhalandar Doddamani ◽  
Mahendar Thudi ◽  
Rajeev K. Varshney
Keyword(s):  
Quality Control ◽  
Next Generation Sequencing ◽  
Large Scale ◽  
Control Analysis ◽  
Next Generation ◽  
Quality Control Analysis ◽  
Illumina Data ◽  
Generation Sequencing

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.

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