Feature point classification and matching

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buir.advisorOnural, Levent
dc.contributor.authorAy, Avşar Polat
dc.date.accessioned2016-01-08T18:03:00Z
dc.date.available2016-01-08T18:03:00Z
dc.date.issued2007
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2007.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2007.en_US
dc.descriptionIncludes bibliographical references leaves 85-105.en_US
dc.description.abstractA feature point is a salient point which can be separated from its neighborhood. Widely used definitions assume that feature points are corners. However, some non-feature points also satisfy this assumption. Hence, non-feature points, which are highly undesired, are usually detected as feature points. Texture properties around detected points can be used to eliminate non-feature points by determining the distinctiveness of the detected points within their neighborhoods. There are many texture description methods, such as autoregressive models, Gibbs/Markov random field models, time-frequency transforms, etc. To increase the performance of feature point related applications, two new feature point descriptors are proposed, and used in non-feature point elimination and feature point sorting-matching. To have a computationally feasible descriptor algorithm, a single image resolution scale is selected for analyzing the texture properties around the detected points. To create a scale-space, wavelet decomposition is applied to the given images and neighborhood scale-spaces are formed for every detected point. The analysis scale of a point is selected according to the changes in the kurtosis values of histograms which are extracted from the neighborhood scale-space. By using descriptors, the detected non-feature points are eliminated, feature points are sorted and with inclusion of conventional descriptors feature points are matched. According to the scores obtained in the experiments, the proposed detection-matching scheme performs more reliable than the Harris detector gray-level patch matching scheme. However, SIFT detection-matching scheme performs better than the proposed scheme.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityAy, Avşar Polaten_US
dc.format.extentxxvii, 186 leaves, graphsen_US
dc.identifier.urihttp://hdl.handle.net/11693/14610
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFeature Point Eliminationen_US
dc.subjectFeature Pointsen_US
dc.subjectFeature Point Detectionen_US
dc.subjectDigital Video Processingen_US
dc.subjectFeature Point Matchingen_US
dc.subject.lccQA274.42 .A9 2007en_US
dc.subject.lcshPoint processes.en_US
dc.subject.lcshMatching theory.en_US
dc.subject.lcshAlgorithms.en_US
dc.subject.lcshImage processing--Digital techniques.en_US
dc.subject.lcshStochastic processes.en_US
dc.titleFeature point classification and matchingen_US
dc.typeThesisen_US

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Dept. of Electrical and Electronics Engineering - Master's degree