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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/17435
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dc.contributor.authorKumar J.-
dc.contributor.authorAnand, Radhey Shyam-
dc.contributor.authorSrivastava S.P.-
dc.date.accessioned2020-12-02T14:30:14Z-
dc.date.available2020-12-02T14:30:14Z-
dc.date.issued2014-
dc.identifier.citationProceedings of 2014 International Conference on Advances in Electrical Engineering, ICAEE 2014, (2014). Vellore, Tamilnadu-
dc.identifier.isbn9.78148E+12-
dc.identifier.urihttps://doi.org/10.1109/ICAEE.2014.6838443-
dc.identifier.urihttp://repository.iitr.ac.in/handle/123456789/17435-
dc.description.abstractThe paper presents a novel approach for multi-class weld flaw classification by means of Gray level co-occurrence matrix (GLCM) based texture feature extraction technique and Artificial Neural Network classifier. The weld radiography films have been digitized using CCD camera, followed by image processing techniques i.e. RGB to gray conversion, region of interest (ROI) selection, noise reduction and contrast enhancement. Subsequently a set of 8, 64 and 44 texture features vectors have been obtained from each of the digitized weld images by means of GLCM. Further, the features obtained have been classified with cascade-forward back propagation neural network. The proposed system has obtained overall classification accuracy of 86.10% for nine different types of weld flaws of digitized radiographic images. © 2014 IEEE.-
dc.language.isoen_US-
dc.publisherIEEE Computer Society-
dc.relation.ispartofProceedings of 2014 International Conference on Advances in Electrical Engineering, ICAEE 2014-
dc.subjectGLCM-
dc.subjectmulti-class classification-
dc.subjectneural network-
dc.subjectradiographic images-
dc.subjecttexture feature-
dc.subjectweld flaws-
dc.titleMulti - Class welding flaws classification using texture feature for radiographic images-
dc.typeConference Paper-
dc.scopusid57210529896-
dc.scopusid56363331000-
dc.scopusid7403307033-
dc.affiliationKumar, J., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India-
dc.affiliationAnand, R.S., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India-
dc.affiliationSrivastava, S.P., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India-
dc.identifier.conferencedetails2014 International Conference on Advances in Electrical Engineering, ICAEE 2014, Vellore, Tamilnadu, 9-11 January 2014-
Appears in Collections:Conference Publications [EE]

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