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dc.contributor.advisorÇetin, A. Enis
dc.contributor.authorBozkurt, Alican
dc.date.accessioned2016-01-08T18:26:04Z
dc.date.available2016-01-08T18:26:04Z
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/11693/15883
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2013.en_US
dc.descriptionIncludes bibliographical references leaves 87-93.en_US
dc.description.abstractAlmost all images that are presented in classification problems regardless of area of application, have directional information embedded into its texture. Although there are many algorithms developed to extract this information, there is no ‘golden’ method that works the best every image. In order to evaluate performance of these developed algorithms, we consider 7 different multi-scale directional feature extraction algorithms along with our own multi-scale directional filtering framework. We perform tests on several problems from diverse areas of application such as font/style recognition on English, Arabic, Farsi, Chinese, and Ottoman texts, grading of follicular lymphoma images, and stratum corneum thickness calculation. We present performance metrics such as k-fold cross validation accuracies and times to extract feature from one sample, and compare with the respective state of art on each problem. Our multi-resolution computationally efficient directional approach provides results on a par with the state of the art directional feature extraction methods.en_US
dc.description.statementofresponsibilityBozkurt, Alicanen_US
dc.format.extentxix, 142 leaves, tablesen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFont recognitionen_US
dc.subjectfollicular lymphoma gradingen_US
dc.subjectstratum corneumen_US
dc.subjectmultiscaleen_US
dc.subjectdirectionalen_US
dc.subjectfeature extractionen_US
dc.subject.lccTA1637 .B69 2013en_US
dc.subject.lcshImage processing.en_US
dc.subject.lcshComputer vision.en_US
dc.subject.lcshOptical pattern recognition.en_US
dc.titleComparison of multi-scale directional feature extraction methods for image processingen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


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