S-Space College of Engineering/Engineering Practice School (공과대학/대학원) Dept. of Materials Science and Engineering (재료공학부) Journal Papers (저널논문_재료공학부)
Automated Defect Detection System using Wavelet Packet Frame and Gaussian Mixture Model
|dc.contributor.author||Kim, Soo Chang||-|
|dc.contributor.author||Kang, Tae Jin||-|
|dc.identifier.citation||J Opt Soc Am A Opt Image Sci Vis..23, 2690-701 (2006)||en|
|dc.description.abstract||This paper proposes an approach for automated defect detection in homogeneous textiles using texture analysis.
The texture features are extracted by the wavelet packet frame decomposition followed by the Karhunen–Loève transform. The texture feature vector for each pixel is used as an input to a Gaussian mixture model
that determines whether or not each pixel is defective. The parameters of the Gaussian mixture model are estimated with nondefective textile images in supervised defect detection. An approach for unsupervised defect detection is also presented that can identify the heterogeneous subblocks on the basis of the Kullback–Leibler divergence between two Gaussian mixtures. The proposed method was evaluated on 25 different homogeneous
textile image pairs, one of each pair with a defect and the other with no defect, and was compared with existing methods using texture analysis. The experimental results yielded visually good segmentation and an excellent
detection rate with a low false alarm rate for both supervised and unsupervised defect detection. This confirms the validity of the proposed approach for automated defect detection and localization.
|dc.description.sponsorship||The authors thank the Korea Science and Engineering Foundation (KOSEF) for sponsoring this research through the SRC/ERC program of MOST/KOSEF (R11-2005-065).||en|
|dc.publisher||Optical Society of America||en|
|dc.title||Automated Defect Detection System using Wavelet Packet Frame and Gaussian Mixture Model||en|
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