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- W2183695623 abstract "Thispaper proposed anewmethod ofextracting cancapture different texture properties oftheimagesat texture features based onGaborwavelet indifferent color space.different levels. Texture classification assigns agiven texture In addition, theapplication ofthesefeatures forbark tosometexture classes (4,5,6,7). Two mainclassification classification applying radialbasisprobabilistic network methodsaresupervised andunsupervised classification. (RBPNN)andSVM(support vector machine) hasbeenused.To Supervised classification isprovided examples ofeachtexture extract thebarktexture features, Gaborfilter theimagehasbeen class asatraining set. A supervised classifier istrained using filtered withfourorientations andsixscales filters, andthenthe thesettolear a characterisation foreachtexture class. meanandstandard deviation oftheimageoutput arecomputed. Inaddition, apart fromthese features ofparameter ofGabor Unsupervised classification doesnotrequire prior knowledge, filter features, other features suchascolor distribution angleswhichisabletoautomatically discover different classes from werealso extracted. Finally, thecombined Gaborfeature vectorsinput textures. Anotherclassissemi-supervised withonly andcolor distribution angles arefedupinto RBPNNandSVM partial prior knowledge being available. forclassification. Theperformance ofcolour space features is Themajority ofclassification methods involve atwo-stage found tobebetter thanthatofthefeatures whichjust extractedprocess. Thefirst stage isbarkimagefeature extraction, which fromgreyimage. Experimental results showthatfeaturesyields acharacterization ofeachbarktexture class interms of extracted using theproposed approach canbeusedforbark feature measures. Itisimportant toidentify andselect texture classification. distinguishing features thatareinvariant toirrelevant transformation ofthebarkimage, suchastranslation, rotation, IndexTerms -Gabor wavelet 'Radial basis probabilistic an cln. Idalyth qunittv mesrsofslce netor,Bak mae,Imgereogiton andscaling. Ideally, thequantitative measuresofselected network'Barkmage,Imageecogntfeatures" @default.
- W2183695623 created "2016-06-24" @default.
- W2183695623 creator A5053768392 @default.
- W2183695623 date "2006-01-01" @default.
- W2183695623 modified "2023-09-26" @default.
- W2183695623 title "BarkClassification UsingRBPNNBasedonGaborFilter inDifferent Color Space" @default.
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- W2183695623 hasPublicationYear "2006" @default.
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