Presentation	2007-10-26 Defect Detection Using k-means Clustering and Eigenspace Outlier Detector Toshiyuki AMANO,
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Abstract(in English)	In this paper, a new approach of defect detection from complex texture patterns that have structural regularity for automated visual inspection is proposed. The approach uses correlation based defect detection, and any kind of model of defect feature is unnecessary. Additionally, proposed method applies k-means clustering to the samples acquired by pixel-by-pixel movement of sampling window, and achieves a very sensitive defect detection. In the experiment, the effectiveness is shown through the experiment by the complex texture patterns that have structural regularity.
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Keyword(in English)	visual inspection / complex texture pattern / local correlation / k-means clustering
Paper #	PRMU2007-111
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Conference Information
Committee	PRMU
Conference Date	2007/10/18(1days)
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Paper Information
Registration To	Pattern Recognition and Media Understanding (PRMU)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Defect Detection Using k-means Clustering and Eigenspace Outlier Detector
Sub Title (in English)
Keyword(1)	visual inspection
Keyword(2)	complex texture pattern
Keyword(3)	local correlation
Keyword(4)	k-means clustering
1st Author's Name	Toshiyuki AMANO
1st Author's Affiliation	Graduate School of Information Science, Nara Institute of Science and Technology()
Date	2007-10-26
Paper #	PRMU2007-111
Volume (vol)	vol.107
Number (no)	281
Page	pp.pp.-
#Pages	6
Date of Issue