Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44467
DC FieldValueLanguage
dc.contributor.authorLee, G.H.en_US
dc.contributor.author陶金旭zh_TW
dc.contributor.authorHsieh, T.W.en_US
dc.contributor.authorTaur, J.en_US
dc.contributor.authorTao, C.W.en_US
dc.date2008zh_TW
dc.date.accessioned2014-06-06T08:12:19Z-
dc.date.available2014-06-06T08:12:19Z-
dc.identifier.issn0091-3286zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/44467-
dc.description.abstractWe propose an a posteriori kernel orthogonal subspace technique to segment texture images. It is a nonlinear version of the signature subspace classifier (SSC) derived on the basis of an unconstrained least-square estimation. In this approach, the linear subspace mixture model for the SSC is first reformulated in feature space via nonlinear mapping. Then the SSC in feature space is kernelized in terms of the kernel functions so that the dot products in the high dimensional feature space can be implicitly calculated by kernels. The obtained kernel SSC (KSSC) is equivalent to a nonlinear SSC in the input space. After that, the KSSC is applied to segment the texture images. To reduce the computational requirement in segmentation, the multiresolution-based technique (MKSSC) is developed. Experimental results demonstrate that the proposed MKSSC approach can effectively segment texture images and outperforms the MSSC method. (C) 2008 Society of Photo-Optical Instrumentation Engineers.en_US
dc.language.isoen_USzh_TW
dc.relationOptical Engineeringen_US
dc.relation.ispartofseriesOptical Engineering, Volume 47, Issue 7.en_US
dc.relation.urihttp://dx.doi.org/10.1117/1.2957049en_US
dc.subjecttexture segmentationen_US
dc.subjectsignature subspace classifieren_US
dc.subjectfuzzy textureen_US
dc.subjectspectrumen_US
dc.subjectprojection approachen_US
dc.subjectneural-networksen_US
dc.subjectimage classificationen_US
dc.titleA posteriori multiresolution-based kernel orthogonal subspace technique for supervised texture segmentationen_US
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1117/1.2957049zh_TW
item.languageiso639-1en_US-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeJournal Article-
item.fulltextno fulltext-
item.grantfulltextnone-
Appears in Collections:電機工程學系所
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