Basit öğe kaydını göster

dc.contributor.advisorEskil, Mustafa Taneren_US
dc.contributor.authorOlzvoi, Uranchimegen_US
dc.contributor.otherIşık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programıen_US
dc.date.accessioned2016-06-22T01:33:37Z
dc.date.available2016-06-22T01:33:37Z
dc.date.issued2013-07-02
dc.identifier.citationOlzvoi, U. (2013). Detecting facial features automatically. İstanbul: Işık Üniversitesi Fen Bilimleri Enstitüsü.en_US
dc.identifier.urihttps://hdl.handle.net/11729/1000
dc.descriptionText in English ; Abstract: English and Turkishen_US
dc.descriptionIncludes bibliographical references (leaves 57-58)en_US
dc.descriptionxii, 59 leavesen_US
dc.descriptionThis research is part of project ”Expression Recognition based on Facial Anatomy”, grant number 109E061, supported by The Support Programme for Scientific and Technological Research Projects of The Scientific and Technological Research Council of Turkey (TUBITAK).en_US
dc.description.abstractThere are many algorithms and approaches in object detection world. Many of them are based on Viola Jones algorithm. According to our observations, the features which help to detect an object are very critical for the success of this algorithm. These features are usually created manually. In this thesis we explore automatic extraction of Haar-like features. We describe the design and construction of a completely automated face detector for gray scale images. Finally, we illustrate the performance of our algorithm on various databases.en_US
dc.description.abstractObje tespit etmek icin bir çok algoritma ve yaklaşım vardır. Bunların çoğu Viola Jones algoritmasına dayanır. Bizim edindiğimiz tecrübelere göre, obje tespitinde temel konu o objeye ait özniteliklerdir. Bu öznitelikler genellikle manuel olarak oluşturulur. Bu tezde biz Haar-like özniteliklerin otomatik çıkarımları üzerine araştırma yaptık. Gri tonlamalı resimler için tamamıyla otomatikleştirilmiş bir yüz algılayıcısı tasarlayıp bunu uyguladık. Nihayetinde, tasarladığımız algoritmanın farklı veribankaları üzerindeki performansını gösterdik.en_US
dc.description.tableofcontentsINTRODUCTIONen_US
dc.description.tableofcontentsHuman Face Detectionen_US
dc.description.tableofcontentsMotivationsen_US
dc.description.tableofcontentsChallengesen_US
dc.description.tableofcontentsApproachen_US
dc.description.tableofcontentsStructure of Thesisen_US
dc.description.tableofcontentsCOMPUTER VISIONen_US
dc.description.tableofcontentsImage Processingen_US
dc.description.tableofcontentsHistogram Equalizationen_US
dc.description.tableofcontentsSmoothingen_US
dc.description.tableofcontentsEdge Detectionen_US
dc.description.tableofcontentsObject Detectionen_US
dc.description.tableofcontentsFace Detectionen_US
dc.description.tableofcontentsFace detection in imagesen_US
dc.description.tableofcontentsReal-time face detectionen_US
dc.description.tableofcontentsHUMAN FACIAL FEATURE DETECTIONen_US
dc.description.tableofcontentsEigenfacesen_US
dc.description.tableofcontentsNeural Networksen_US
dc.description.tableofcontentsVIOLA-JONESen_US
dc.description.tableofcontentsHaar Like Featuresen_US
dc.description.tableofcontentsThresholdingen_US
dc.description.tableofcontentsParityen_US
dc.description.tableofcontentsIntegral Imageen_US
dc.description.tableofcontentsAdaBoost Algorithmen_US
dc.description.tableofcontentsDerivated AdaBoost in the Viola-Jones methoden_US
dc.description.tableofcontentsCascadeen_US
dc.description.tableofcontentsConclusionen_US
dc.description.tableofcontentsIMPLEMENTATIONen_US
dc.description.tableofcontentsCodingen_US
dc.description.tableofcontentsPreprocessingen_US
dc.description.tableofcontentsFace Detection Algorithmen_US
dc.description.tableofcontentsImplementation Challengesen_US
dc.description.tableofcontentsXOR Logic Operationen_US
dc.description.tableofcontentsAND Logic Operationen_US
dc.description.tableofcontentsOR Logic Operationen_US
dc.description.tableofcontentsCovarianceen_US
dc.description.tableofcontentsDatabaseen_US
dc.description.tableofcontentsThe Importance of a Databaseen_US
dc.description.tableofcontentsOur chosen Databasesen_US
dc.description.tableofcontentsRESULTSen_US
dc.description.tableofcontentsBruteforceen_US
dc.description.tableofcontentsCovarianceen_US
dc.description.tableofcontentsSuper Featureen_US
dc.description.tableofcontentsBest N Featureen_US
dc.description.tableofcontentsBestNFeat + Majorityen_US
dc.description.tableofcontentsAND-OR-XORen_US
dc.description.tableofcontentsConclusionen_US
dc.description.tableofcontentsDISCUSSION AND CONCLUSIONSen_US
dc.description.tableofcontentsDiscussion and Conclusionsen_US
dc.description.tableofcontentsFuture Worken_US
dc.language.isoengen_US
dc.publisherIşık Üniversitesien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subject.lccTA1650 .O49 2013
dc.subject.lcshComputer engineering.en_US
dc.subject.lcshFacial expression.en_US
dc.subject.lcshSign language.en_US
dc.subject.lcshHuman face recognition (Computer science)en_US
dc.subject.lcshPattern recognition systems.en_US
dc.titleDetecting facial features automaticallyen_US
dc.typemasterThesisen_US
dc.contributor.departmentIşık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programıen_US
dc.relation.publicationcategoryTezen_US
dc.contributor.institutionauthorOlzvoi, Uranchimegen_US


Bu öğenin dosyaları:

Thumbnail

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster

info:eu-repo/semantics/openAccess
Aksi belirtilmediği sürece bu öğenin lisansı: info:eu-repo/semantics/openAccess