Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/9571
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dc.contributor.authorKöksal, Ali-
dc.contributor.authorÖzuysal, Mustafa-
dc.date.accessioned2020-07-25T22:17:41Z-
dc.date.available2020-07-25T22:17:41Z-
dc.date.issued2019-
dc.identifier.issn1751-9632-
dc.identifier.issn1751-9640-
dc.identifier.urihttps://doi.org/10.1049/iet-cvi.2018.5613-
dc.identifier.urihttps://hdl.handle.net/11147/9571-
dc.descriptionOZUYSAL, MUSTAFA/0000-0003-0257-6882en_US
dc.descriptionWOS: 000479306100008en_US
dc.description.abstractThe authors propose a novel approach for the description of objects based on contours in their images using real-valued feature vectors. The approach is particularly suitable when objects of interest have high contrast and texture-free images or when the texture variations are high so textural cues are nuisance factors for classification. The proposed descriptor is suitable for nearest neighbour classification still popular in embedded vision applications when the power considerations outweigh the performance requirements. They describe object outlines purely based on the histograms of contour tangent directions mimicking many of the design heuristics of texture-based descriptors such as scale-invariant feature transform (SIFT). However, unlike SIFT and its variants, the proposed approach is directly designed to work with contour data and it is robust to variations inside and outside the object outline as well as the sampling of the contour itself. They show that relying on tangent direction estimation as opposed to gradient computation yields a more robust description and higher nearest neighbour classification rates in a variety of classification problems.en_US
dc.language.isoenen_US
dc.publisherInstitution of Engineering and Technologyen_US
dc.relation.ispartofIET Computer Visionen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjecttransformsen_US
dc.subjectimage classificationen_US
dc.subjectimage textureen_US
dc.subjectfeature extractionen_US
dc.subjectnearest neighbour methodsen_US
dc.subjectimage samplingen_US
dc.subjectgradient methodsen_US
dc.subjecttexture-free imagesen_US
dc.subjecttexture variationsen_US
dc.subjecttextural cuesen_US
dc.subjectembedded vision applicationsen_US
dc.subjecttexture-based descriptorsen_US
dc.subjectSIFTen_US
dc.subjecttangent direction estimationen_US
dc.subjectscale-invariant feature transformen_US
dc.subjectnearest neighbour classificationen_US
dc.titleCurve description by histograms of tangent directionsen_US
dc.typeArticleen_US
dc.departmentIzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.volume13en_US
dc.identifier.issue5en_US
dc.identifier.startpage507en_US
dc.identifier.endpage514en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.cont.department-temp[Koksal, Ali; Ozuysal, Mustafa] Izmir Inst Technol, Dept Comp Engn, Izmir, Turkeyen_US
dc.identifier.doi10.1049/iet-cvi.2018.5613-
dc.relation.doi10.1049/iet-cvi.2018.5613en_US
dc.coverage.doi10.1049/iet-cvi.2018.5613en_US
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.grantfulltextnone-
crisitem.author.deptDepartment of Computer Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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