DEFECT CLASSIFICATION FOR LCD COLOR FILTERS USING NEURAL-NETWORK DECISION TREE CLASSIFIER

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/51624

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51624

Title:	DEFECT CLASSIFICATION FOR LCD COLOR FILTERS USING NEURAL-NETWORK DECISION TREE CLASSIFIER
Authors:	Tseng,DC;Chung,IL;Tsai,PL;Chou,CM
Contributors:	資訊工程學系
Keywords:	INDEPENDENT COMPONENT ANALYSIS;RECOGNITION;SURFACES;MACHINE
Date:	2011
Issue Date:	2012-03-27 18:57:45 (UTC+8)
Publisher:	國立中央大學
Abstract:	A real-time automatic defect classification scheme is proposed to understand the situation of color-filter production procedure by analyzing the defect types. The proposed system consists of three stages: defect extraction, feature description, and defect-type classification. A reflex-lighted image and the same-area back-lighted image are both used for the defect classification. Four features extracted from the reflex-lighted images describe the shape of the defects and other four features extracted from the back-lighted images describe the appearance of the defects on the color-filter cells. A neural-network decision tree classifier is then designed for classifying the defect types. The neural network has the ability to analyze the complicated non-linear relationship between input signals and the desired outputs. A back propagation algorithm is used as the training method for the proposed classifier. The experimental results show that the proposed scheme can efficiently classify the defect types and the proposed neural-network classifier is superior to other classifiers.
Relation:	INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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