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dc.contributor.authorÇipiloğlu Yıldız, Zeynep
dc.contributor.authorÖztireli, A. Cengiz
dc.contributor.authorCapin, Tolga
dc.date.accessioned2020-07-02T07:45:23Z
dc.date.available2020-07-02T07:45:23Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/20.500.12481/12276
dc.description.abstractTo decide whether the perceived quality of a mesh is influenced by a certain modification such as compressionors implification, a metric for estimating the visual quality of 3D meshes is required. Today, machine learning and deep learning techniques are getting increasingly popular since they present efficient solutions to many complex problems. However, these techniques are not much utilized in the field of 3D shape perception. We propose a novel machine learning-based approach for evaluating the visual quality of 3D static meshes. The novelty of our study lies in incorporating crowdsourcing in a machine learning framework for visual quality evaluation. We deliberate that this is an elegant way since modeling human visual system processes is a tedious task and requires tuning many parameters. We employ crowdsourcing methodology for collecting data of quality evaluations and metric learning for drawing the best parameters that well correlate with the human perception. Experimental validation of the proposed metric reveals a promising correlation between the metric output and human perception. Results of our crowdsourcing experiments are publicly available for the community.tr_TR
dc.language.isoentr_TR
dc.publisherSpringer Berlin Heidelbergtr_TR
dc.rightsinfo:eu-repo/semantics/openAccesstr_TR
dc.subjectVisual quality assessmenttr_TR
dc.subjectMesh qualitytr_TR
dc.subjectPerceptual computer graphicstr_TR
dc.subjectCrowdsourcingtr_TR
dc.subjectMetric learningtr_TR
dc.titleA machine learning framework for full-reference 3D shape quality assessmenttr_TR
dc.typeMakaletr_TR
dc.contributor.MCBUauthorÇipiloğlu Yıldız, Zeynep
dc.contributor.departmentFakülteler > Mühendislik Fakültesi > Bilgisayar Mühendisliğitr_TR
dc.identifier.ORC-ID0000-0003-4129-591Xtr_TR
dc.identifier.categoryOfPublishedMaterialMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıtr_TR
dc.identifier.nameOfPublishedMaterialSpringertr_TR
dc.identifier.DOI-IDhttps://doi.org/10.1007/s00371-018-1592-9tr_TR
dc.identifier.volume36tr_TR
dc.identifier.issue1tr_TR
dc.identifier.startpage127tr_TR
dc.identifier.endpage139tr_TR
dc.description.bibliographicÇipiloğlu Yıldız, Z., Öztireli A. C. ve Capin, T. (2018), A machine learning framework for full-reference 3D shape quality assessment, Almanya: Springer.tr_TR
dc.identifier.indicesScopus (DOI)tr_TR
dc.identifier.issn/e-issn01782789


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