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dc.contributor.authorJoo, Hyonamen
dc.date.accessioned2015-06-23T19:08:27Zen
dc.date.available2015-06-23T19:08:27Zen
dc.date.issued1985en
dc.identifier.urihttp://hdl.handle.net/10919/53076en
dc.description.abstractTwo methods of designing a point classifier are discussed in this paper, one is a binary decision tree classifier based on the Fisher's linear discriminant function as a decision rule at each nonterminal node, and the other is a contextual classifier which gives each pixel the highest probability label given some substantially sized context including the pixel. Experiments were performed both on a simulated image and real images to illustrate the improvement of the classification accuracy over the conventional single-stage Bayes classifier under Gaussian distribution assumption.en
dc.format.extentvi, 93 leavesen
dc.format.mimetypeapplication/pdfen
dc.language.isoen_USen
dc.publisherVirginia Polytechnic Institute and State Universityen
dc.relation.isformatofOCLC# 12655126en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V855 1985.J67en
dc.subject.lcshPattern recognition systemsen
dc.subject.lcshImage processing -- Experimentsen
dc.titleBinary tree classifier and context classifieren
dc.typeThesisen
dc.contributor.departmentElectrical Engineeringen
dc.description.degreeMaster of Scienceen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelmastersen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineElectrical Engineeringen
dc.type.dcmitypeTexten


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