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Automatic detection of image orientation using Support Vector Machines
Thesis (MSc)--University of Stellenbosch, 2002.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2012
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| _version_ | 1867613915752431616 |
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| access_status_str | Open Access |
| author | Walsh, Dane A. |
| author2 | Omlin, C. W. |
| author_browse | Omlin, C. W. Walsh, Dane A. |
| author_facet | Omlin, C. W. Walsh, Dane A. |
| author_sort | Walsh, Dane A. |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MSc)--University of Stellenbosch, 2002. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/52717 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:43:44.261Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2012 |
| publishDateRange | 2012 |
| publishDateSort | 2012 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/52717 Automatic detection of image orientation using Support Vector Machines Walsh, Dane A. Omlin, C. W. Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences (applied, computer, mathematics). Image processing Vector processing (Computer science) Kernel functions Dissertations -- Computer science Theses -- Computer science Thesis (MSc)--University of Stellenbosch, 2002. ENGLISH 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. AFRIKAANSE 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. 2012-08-27T11:35:07Z 2012-08-27T11:35:07Z 2002-12 Thesis http://hdl.handle.net/10019.1/52717 en_ZA Stellenbosch University 79 p. : ill. application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Image processing Vector processing (Computer science) Kernel functions Dissertations -- Computer science Theses -- Computer science Walsh, Dane A. Automatic detection of image orientation using Support Vector Machines |
| title | Automatic detection of image orientation using Support Vector Machines |
| title_full | Automatic detection of image orientation using Support Vector Machines |
| title_fullStr | Automatic detection of image orientation using Support Vector Machines |
| title_full_unstemmed | Automatic detection of image orientation using Support Vector Machines |
| title_short | Automatic detection of image orientation using Support Vector Machines |
| title_sort | automatic detection of image orientation using support vector machines |
| topic | Image processing Vector processing (Computer science) Kernel functions Dissertations -- Computer science Theses -- Computer science |
| url | http://hdl.handle.net/10019.1/52717 |
| work_keys_str_mv | AT walshdanea automaticdetectionofimageorientationusingsupportvectormachines |