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dc.contributor.authorGhosh, Anindya-
dc.contributor.authorGuha, Tarit-
dc.contributor.authorBhar, R B-
dc.identifier.issn0975-1025 (Online); 0971-0426 (Print)-
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.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
Appears in Collections:IJFTR Vol.40(1) [March 2015]

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