Arc-welding defect detection by means of principal component analysis and artificial neural networks

2007 ◽  
Author(s):  
P. B. García-Allende ◽  
J. Mirapeix ◽  
A. Cobo ◽  
O. M. Conde ◽  
J M. López-Higuera
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Artificial Neural Networks ◽  
Defect Detection ◽  
Arc Welding ◽  
Principal Component ◽  
Component Analysis ◽  
Artificial Neural ◽  
Welding Defect

scholarly journals Artificial Neural Networks for Automated Cell Quantification in Lensless LED Imaging Systems

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 989 ◽  
Author(s):  
Agus Budi Dharmawan ◽  
Gregor Scholz ◽  
Shinta Mariana ◽  
Philipp Hörmann ◽  
Igi Ardiyanto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Artificial Neural Networks ◽  
Light Emitting Diode ◽  
Principal Component ◽  
Component Analysis ◽  
Detection Accuracy ◽  
Cell Detection ◽  
Artificial Neural ◽  
Feature Values

Cell registration by artificial neural networks (ANNs) in combination with principal component analysis (PCA) has been demonstrated for cell images acquired by light emitting diode (LED)-based compact holographic microscopy. In this approach, principal component analysis was used to find the feature values from cells and background, which would be subsequently employed as neural inputs into the artificial neural networks. Image datasets were acquired from multiple cell cultures using a lensless microscope, where the reference data was generated by a manually analyzed recording. To evaluate the developed automatic cell counter, the trained system was assessed on different data sets to detect immortalized mouse astrocytes, exhibiting a detection accuracy of ~81% compared with manual analysis. The results show that the feature values from principal component analysis and feature learning by artificial neural networks are able to provide an automatic approach on the cell detection and registration in lensless holographic imaging.

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