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Article: Fabric defect detection using multi-level tuned-matched gabor filters
Title | Fabric defect detection using multi-level tuned-matched gabor filters | ||||
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Authors | |||||
Keywords | Defect Detection Industrial Inspection Multi-Level Gabor Wavelet Woven Fabrics | ||||
Issue Date | 2012 | ||||
Publisher | American Institute of Mathematical Sciences. The Journal's web site is located at http://aimsciences.org/journals/jimo/description.htm | ||||
Citation | Journal Of Industrial And Management Optimization, 2012, v. 8 n. 2, p. 325-341 How to Cite? | ||||
Abstract | This paper proposes a new defect detection scheme for woven fab- rics. The proposed scheme is divided into two parts, namely the training part and the defect detection part. In the training part, a non-defective fabric image is used as a template image, and a finite set of multi-level Gabor wavelets are tuned to match the texture information of the image. In the defect detection part, filtered images from different levels are fused together and the constructed detection scheme is used to detect defects in fabric sample images with the same texture background as that of the template image. A filter selection method is also developed to select optimal filters to facilitate defect detection. The nov- elty of the method comes from the observation that a Gabor filter with finer resolutions than the fabric defects yarn can contribute very little for defect segmentation but need additional computational time. The proposed scheme is tested by using 78 homogeneous textile fabric images. The results exhibit ac- curate defect detections with lower false alarms than using the standard Gabor wavelets. Analysis of the computational complexity of the proposed detection scheme is derived, which shows that the scheme can be implemented in real time easily. | ||||
Persistent Identifier | http://hdl.handle.net/10722/155964 | ||||
ISSN | 2023 Impact Factor: 1.2 2023 SCImago Journal Rankings: 0.364 | ||||
ISI Accession Number ID |
Funding Information: The authors gratefully acknowledge the financial support from the Research Grants Council of the Hong Kong Special Administrative Region, PRC under the grant HKU 714807E for this project. | ||||
References | |||||
Grants |
DC Field | Value | Language |
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dc.contributor.author | Mak, KL | en_US |
dc.contributor.author | Peng, P | en_US |
dc.contributor.author | Yiu, KFC | en_US |
dc.date.accessioned | 2012-08-08T08:38:39Z | - |
dc.date.available | 2012-08-08T08:38:39Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.citation | Journal Of Industrial And Management Optimization, 2012, v. 8 n. 2, p. 325-341 | en_US |
dc.identifier.issn | 1547-5816 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/155964 | - |
dc.description.abstract | This paper proposes a new defect detection scheme for woven fab- rics. The proposed scheme is divided into two parts, namely the training part and the defect detection part. In the training part, a non-defective fabric image is used as a template image, and a finite set of multi-level Gabor wavelets are tuned to match the texture information of the image. In the defect detection part, filtered images from different levels are fused together and the constructed detection scheme is used to detect defects in fabric sample images with the same texture background as that of the template image. A filter selection method is also developed to select optimal filters to facilitate defect detection. The nov- elty of the method comes from the observation that a Gabor filter with finer resolutions than the fabric defects yarn can contribute very little for defect segmentation but need additional computational time. The proposed scheme is tested by using 78 homogeneous textile fabric images. The results exhibit ac- curate defect detections with lower false alarms than using the standard Gabor wavelets. Analysis of the computational complexity of the proposed detection scheme is derived, which shows that the scheme can be implemented in real time easily. | en_US |
dc.language | eng | en_US |
dc.publisher | American Institute of Mathematical Sciences. The Journal's web site is located at http://aimsciences.org/journals/jimo/description.htm | en_US |
dc.relation.ispartof | Journal of Industrial and Management Optimization | en_US |
dc.subject | Defect Detection | en_US |
dc.subject | Industrial Inspection | en_US |
dc.subject | Multi-Level Gabor Wavelet | en_US |
dc.subject | Woven Fabrics | en_US |
dc.title | Fabric defect detection using multi-level tuned-matched gabor filters | en_US |
dc.type | Article | en_US |
dc.identifier.email | Mak, KL:makkl@hkucc.hku.hk | en_US |
dc.identifier.email | Yiu, KFC:cedric@hkucc.hku.hk | en_US |
dc.identifier.authority | Mak, KL=rp00154 | en_US |
dc.identifier.authority | Yiu, KFC=rp00206 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.3934/jimo.2012.8.325 | en_US |
dc.identifier.scopus | eid_2-s2.0-84861763158 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-84861763158&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 8 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.spage | 325 | en_US |
dc.identifier.epage | 341 | en_US |
dc.identifier.isi | WOS:000304007400004 | - |
dc.publisher.place | United States | en_US |
dc.relation.project | A tensor-based decomposition technique for detecting textile fabric defect occurrence | - |
dc.identifier.scopusauthorid | Mak, KL=7102680226 | en_US |
dc.identifier.scopusauthorid | Peng, P=55237566500 | en_US |
dc.identifier.scopusauthorid | Yiu, KFC=24802813000 | en_US |
dc.identifier.issnl | 1547-5816 | - |