Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7456
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dc.contributor.authorGürbüz, Ali Cafer-
dc.date.accessioned2021-09-11T15:57:08Z-
dc.date.available2021-09-11T15:57:08Z-
dc.date.issued2009en_US
dc.identifier.citation24th International Symposium on Computer and Information Sciences -- SEP 14-16, 2009 -- Guzelyurt, CYPRUSen_US
dc.identifier.isbn978-1-4244-5021-3-
dc.identifier.urihttps://doi.org/10.1109/ISCIS.2009.5291916-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/7456-
dc.description.abstractDetection of different kinds of shapes, i.e. lines, circles, hyperbolas etc., in varying kinds of images arises in diverse areas such as signal and image processing, computer vision or remote sensing. The generalized Hough Transform is a traditional approach to detect a specific shape in an image by transforming the problem into a parameter space representation. In this paper we use the observation that the number of shapes in an image is much smaller than the number of all possible shapes. This means the shapes are sparse in the parameter domain. Rather than forming the parameter space from the image as in the HT, we take a reverse approach and ask "which combination of parameter space cells represent my data best?". This leads us to generate a dictionary of shapes and use additional information about sparsity of shapes within a basis pursuit framework. The results indicate enhanced shape detection performance, increased resolution, joint detection of different shapes in an image and robustness to noise. In addition to this, combining the sparsity of shapes with the Compressive Sensing ideas shows that it is possible to directly find the shapes in an image from small number of random projections of the image without first reconstructing the image itself.en_US
dc.description.sponsorshipMiddle E Tech Univen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2009 24Th International Symposium On Computer And Information Sciencesen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSparsityen_US
dc.subjectHough Transformen_US
dc.subjectShape Detectionen_US
dc.subjectBasis pursuiten_US
dc.subjectConvex optimizationen_US
dc.subjectCompressed sensingen_US
dc.subjectLine detectionen_US
dc.titleShape Detection in Images Exploiting Sparsityen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.identifier.startpage70en_US
dc.identifier.endpage75en_US
dc.authorid0000-0001-8923-0299-
dc.authorid0000-0001-8923-0299-
dc.identifier.wosWOS:000275024200014en_US
dc.identifier.scopus2-s2.0-73949120112en_US
dc.institutionauthorGürbüz, Ali Cafer-
dc.identifier.doi10.1109/ISCIS.2009.5291916-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conference24th International Symposium on Computer and Information Sciencesen_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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