Automatic detection of image orientation using Support Vector Machines

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dc.contributor.advisorOmlin, C. W.en_ZA
dc.contributor.authorWalsh, Dane A.en_ZA
dc.contributor.otherStellenbosch University. Faculty of Science. Dept. of Mathematical Sciences (applied, computer, mathematics).en_ZA
dc.date.accessioned2012-08-27T11:35:07Z
dc.date.available2012-08-27T11:35:07Z
dc.date.issued2002-12
dc.descriptionThesis (MSc)--University of Stellenbosch, 2002.en_ZA
dc.description.abstractENGLISH ABSTRACT: In this thesis, we present a technique for the automatic detection of image orientation using Support Vector Machines (SVMs). SVMs are able to handle feature spaces of high dimension and automatically choose the most discriminative features for classification. We investigate the use of various kernels, including heavy tailed RBF kernels. We compare the classification performance of SVMs with the performance of multilayer perceptrons and a Bayesian classifier. Our results show that SVMs out perform both of these methods in the classification of individual images. We also implement an application for the classification of film rolls in a photographic workflow environment with 100% classification accuracy.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: In hierdie tesis, gebruik ons 'n tegniek vir die automatiese klassifisering van beeldoriëntasie deur middel van Support Vector Machines (SVM's). SVM's kan kenmerkruimtes van 'n hoë dimensie hanteer en kan automaties die mees belangrike kenmerke vir klassifikasie kies. Ons vors die gebruik van verskeie kerne, insluitende RBF-kerne, na. Ons vergelyk die klassifiseringsresultate van SVM's met die van multilaagperseptrone en 'n Bayes-klassifiseerder. Ons bewys dat SVM's beter resultate gee as beide van hierdie metodes vir die klassifikasie van individuele beelde. Ons implementeer ook a toepassing vir die klassifisering van rolle film in a fotografiese werkvloei-omgewing met 100% klassifikasie akuraatheid.af_ZA
dc.format.extent79 p. : ill.
dc.identifier.urihttp://hdl.handle.net/10019.1/52717
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subjectImage processingen_ZA
dc.subjectVector processing (Computer science)en_ZA
dc.subjectKernel functionsen_ZA
dc.subjectDissertations -- Computer scienceen_ZA
dc.subjectTheses -- Computer scienceen_ZA
dc.titleAutomatic detection of image orientation using Support Vector Machinesen_ZA
dc.typeThesisen_ZA
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