Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/31072
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dc.contributor.authorGhosh, Anindya-
dc.contributor.authorGuha, Tarit-
dc.contributor.authorBhar, R B-
dc.date.accessioned2015-03-23T06:20:59Z-
dc.date.available2015-03-23T06:20:59Z-
dc.date.issued2015-03-
dc.identifier.issn0975-1025 (Online); 0971-0426 (Print)-
dc.identifier.urihttp://hdl.handle.net/123456789/31072-
dc.description87-93en_US
dc.description.abstractThis study endeavors to recognize handloom and powerloom products by means of proximal support vector machine (PSVM) using the features extracted from gray level images of both fabrics. A k-fold cross validation technique has been applied to assess the accuracy. The robustness, speed of execution, proven accuracy coupled with simplicity in algorithm hold the PSVM as a foremost classifier to recognize handloom and powerloom fabrics.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceIJFTR Vol.40(1) [March 2015]en_US
dc.subjectHandloom fabricsen_US
dc.subjectImage processingen_US
dc.subjectPattern classificationen_US
dc.subjectProximal support vector machineen_US
dc.subjectPowerloom fabricsen_US
dc.titleIdentification of handloom and powerloom fabrics using proximal support vector machinesen_US
dc.typeArticleen_US
Appears in Collections:IJFTR Vol.40(1) [March 2015]

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