Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/1369
Title: Wavelet-based neural network and statistical approaches applied to automated visual inspection of LED chips
Authors: Lin, Hong-Dar
Lin, Gary C
Chung, Chung-Yu
Lin, Wan-Ting
Keywords: Automated visual inspection;Back-propagation network;Hotelling statistic;LED chip production;Wavelet characteristics
Issue Date: Jun-2008
Publisher: CSIR
Abstract: This research explores automated visual inspection of surface defects in a light-emitting diode (LED) chip. One-level Haar wavelet transform is first used to decompose a chip image and extract four wavelet characteristics. Then, wavelet-based back-propagation network (WBPN) and wavelet-based Hotelling statistic (WHS) approaches are respectively applied to integrate multiple wavelet characteristics. Finally, back-propagation algorithm of WBPN or Hotelling test of WHS judges existence of defects. Two proposed methods achieve detection rates of above 90.8% and 92.4%, and false alarm rates below 4.4% and 6.1%, respectively. A valid computer-aided visual defect inspection system is contributed to help meet quality control needs of LED chip manufacturers.
Page(s): 412-420
URI: http://hdl.handle.net/123456789/1369
ISSN: 0022-4456
Appears in Collections: JSIR Vol.67(06) [June 2008]

Files in This Item:
File Description SizeFormat 
JSIR 67(6) (2008) 412-420.pdf238.92 kBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.