Sun position esimation on time-lapse videos for augmented reality applications

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buir.advisorGüdükbay, Uğur
dc.contributor.authorBalcı, Hasan
dc.date.accessioned2016-07-20T12:57:02Z
dc.date.available2016-07-20T12:57:02Z
dc.date.copyright2015-07
dc.date.issued2015-07
dc.date.submitted2015-08-05
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2015.en_US
dc.descriptionIncludes bibliographical references (leaves 40-45).en_US
dc.description.abstractRealistic illumination of virtual objects on Augmented Reality (AR) environments is important in terms of achieving visual coherence. This thesis proposes a novel approach that facilitates the illumination estimation on time-lapse videos and gives the opportunity to combine AR technology with time-lapse videos in a visually consistent way. The proposed approach works for both outdoor and indoor environments where the main light source is the Sun. We rst modify an existing illumination estimation method that aims to obtain sparse radiance map of the environment in order to estimate the initial Sun position. We then track the hard ground shadows on the time-lapse video by using an energy-based pixelwise method. The proposed method aims to track the shadows by utilizing the energy values of the pixels that forms them. We tested the method on various time-lapse videos recorded in outdoor and indoor environments and obtained successful results.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2016-07-20T12:57:02Z No. of bitstreams: 1 10083521.pdf: 2573072 bytes, checksum: 1f08fd9332e22875fc5d34d13bc1825f (MD5)en
dc.description.provenanceMade available in DSpace on 2016-07-20T12:57:02Z (GMT). No. of bitstreams: 1 10083521.pdf: 2573072 bytes, checksum: 1f08fd9332e22875fc5d34d13bc1825f (MD5) Previous issue date: 2015-07en
dc.description.statementofresponsibilityby Hasan Balcı.en_US
dc.embargo.release2017-08-01
dc.format.extentx, 45 leaves. : charts.en_US
dc.identifier.itemidB150945
dc.identifier.urihttp://hdl.handle.net/11693/30149
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSun position estimationen_US
dc.subjectLight source position estimationen_US
dc.subjectIllumination estimationen_US
dc.subjectTime-lapse videoen_US
dc.subjectShadow trackingen_US
dc.subjectAugmented realityen_US
dc.titleSun position esimation on time-lapse videos for augmented reality applicationsen_US
dc.title.alternativeArtırılmış gerçeklik uygulamaları için hızlandırılmış çekim videolarda güneş pozisyonu tahminien_US
dc.typeThesisen_US
thesis.degree.disciplineComputer Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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