File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Defect detection in textured materials using Gabor filters

TitleDefect detection in textured materials using Gabor filters
Authors
KeywordsEngineering
Electrical engineering
Issue Date2000
PublisherIEEE.
Citation
The 35th IEEE - IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy; Rome, Italy, 8-12 October 2000. In Industry Applications Society. IEEE - IAS Annual Meeting Conference Record, 2000, v. 2, p. 1041-1047 How to Cite?
AbstractVision-based inspection of industrial materials such as textile webs, paper or wood requires the development of defect segmentation techniques based on texture analysis. In this work, a multi-channel filtering technique that imitates the early human vision process is applied to images captured online. This new approach uses Bernoulli's rule of combination for integrating images from different channels. Physical image size and yarn impurities are used as key parameters for tuning the sensitivity of the proposed algorithm. Several real fabric samples along with the result of segmented defects are presented. The results achieved show that the developed algorithm is robust, scalable and computationally efficient for detection of local defects in textured materials.
Persistent Identifierhttp://hdl.handle.net/10722/46276
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorPathak, AKen_HK
dc.contributor.authorPang, GKHen_HK
dc.date.accessioned2007-10-30T06:46:19Z-
dc.date.available2007-10-30T06:46:19Z-
dc.date.issued2000en_HK
dc.identifier.citationThe 35th IEEE - IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy; Rome, Italy, 8-12 October 2000. In Industry Applications Society. IEEE - IAS Annual Meeting Conference Record, 2000, v. 2, p. 1041-1047en_HK
dc.identifier.issn0197-2618en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46276-
dc.description.abstractVision-based inspection of industrial materials such as textile webs, paper or wood requires the development of defect segmentation techniques based on texture analysis. In this work, a multi-channel filtering technique that imitates the early human vision process is applied to images captured online. This new approach uses Bernoulli's rule of combination for integrating images from different channels. Physical image size and yarn impurities are used as key parameters for tuning the sensitivity of the proposed algorithm. Several real fabric samples along with the result of segmented defects are presented. The results achieved show that the developed algorithm is robust, scalable and computationally efficient for detection of local defects in textured materials.en_HK
dc.format.extent1168261 bytes-
dc.format.extent4353 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIndustry Applications Society. IEEE - IAS Annual Meeting Conference Record-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectEngineeringen_HK
dc.subjectElectrical engineeringen_HK
dc.titleDefect detection in textured materials using Gabor filtersen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0197-2618&volume=2&spage=1041&epage=1047&date=2000&atitle=Defect+detection+in+textured+materials+using+Gabor+filtersen_HK
dc.identifier.emailPang, Grantham: gpang@eee.hku.hk-
dc.identifier.authorityPang, Grantham=rp00162-
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/IAS.2000.881960en_HK
dc.identifier.scopuseid_2-s2.0-0034509750-
dc.identifier.hkuros60028-
dc.identifier.hkuros62084-
dc.identifier.hkuros72393-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0034509750&selection=ref&src=s&origin=recordpage-
dc.identifier.volume2-
dc.identifier.spage1041-
dc.identifier.epage1047-
dc.publisher.placeUnited States-
dc.identifier.scopusauthoridKumar, Ajay=35198218300-
dc.identifier.scopusauthoridPang, Grantham=7103393283-
dc.customcontrol.immutablesml 160112 - merged-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats