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dc.contributor.authorLin, Hong-Dar-
dc.contributor.authorLin, Gary C-
dc.contributor.authorChung, Chung-Yu-
dc.contributor.authorLin, Wan-Ting-
dc.description.abstractThis 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.en_US
dc.sourceJSIR Vol.67(6) [June 2008]en_US
dc.subjectAutomated visual inspectionen_US
dc.subjectBack-propagation networken_US
dc.subjectHotelling statisticen_US
dc.subjectLED chip productionen_US
dc.subjectWavelet characteristicsen_US
dc.titleWavelet-based neural network and statistical approaches applied to automated visual inspection of LED chipsen_US
Appears in Collections: JSIR Vol.67(06) [June 2008]

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