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dc.contributor.authorHaniza, Yazid, Dr.-
dc.contributor.authorHamzah, Arof, Dr.-
dc.contributor.authorHafizal, Yazid-
dc.contributor.authorSahrim, Ahmad-
dc.contributor.authorAbdul Aziz, Mohamed-
dc.contributor.authorFauzan, Ahmad-
dc.date.accessioned2014-04-30T05:07:56Z-
dc.date.available2014-04-30T05:07:56Z-
dc.date.issued2011-
dc.identifier.citationNDT and E International, vol. 44(7), 2011, pages 563-570en_US
dc.identifier.issn0963-8695-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0963869511000752-
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/34191-
dc.descriptionLink to publisher's homepage at http://www.sciencedirect.com/en_US
dc.description.abstractAutomated detection of welding defects in radiographic images becomes nontrivial when uneven illumination, contrast and noise are present. In this paper, a new approach using surface thresholding method is proposed to detect defects in radiographic images of welding joints. In the first stage, several image processing techniques namely fuzzy c means clustering, region filling, mean filtering, edge detection, Otsu thresholding, and morphological operations method are utilized to locate the area where defects might exist. This is followed by the construction of the inverse thresholding surface and its implementation to locate defects in the identified area. The proposed method was tested on 60 radiographic images and it obtained 94.6% sensitivity. Its performance is compared to that of the watershed segmentation, which obtained 69.6%.en_US
dc.language.isoenen_US
dc.publisherElsevier Ltd.en_US
dc.subjectFuzzy c means clusteringen_US
dc.subjectInverse surface thresholdingen_US
dc.subjectNon-destructive testingen_US
dc.subjectWelded jointsen_US
dc.titleDiscontinuities detection in welded joints based on inverse surface thresholdingen_US
dc.typeArticleen_US
dc.identifier.urlhttp://dx.doi.org/10.1016/j.ndteint.2011.06.002-
dc.contributor.urlhaniza.yazid@gmail.comen_US
Appears in Collections:Haniza Yazid, Dr.

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