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dc.contributor.authorKöse, Kıvançen_US
dc.contributor.authorÇetin, Ahmet Enisen_US
dc.coverage.spatialAntalya, Turkeyen_US
dc.date.accessioned2016-02-08T12:28:02Z
dc.date.available2016-02-08T12:28:02Z
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/11693/28724
dc.descriptionDate of Conference: 9-11 April 2009en_US
dc.descriptionConference Name: IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009en_US
dc.description.abstractIn the last few years, there is great increase in capture and representation of real 3-Dimensonal scenes using 3D animation models. The 3D signals are then compressed, transmitted to the client side and reconstructed for the user view. Each step mentioned here opened a new subject in the field of signal processing. While processing these models, using the model as a whole is not the best approach. Therefore clustering the model vertices became a very common method. For example, it is very common to use motion based clustering in animation compression. In this paper a new dynamic model clustering algorithm is proposed. Animation vertices are first put through PCA and partitioned into their eigenvalues and eigenvectors. The eigenvectors found using the proposed method can be called eigentrajectories. Then the dot product of the these eigentrajectories with the trajectories of the animation vertice are found. These coefficients are used to cluster the animation model. The results and the comparisons with a similar approach show that the proposed algorithm is successful.en_US
dc.language.isoTurkishen_US
dc.source.titleProceedings of the IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2009.5136396en_US
dc.subject3D animationen_US
dc.subjectAnimation compressionen_US
dc.subjectEigenvalues and eigenvectorsen_US
dc.subjectClustering algorithmsen_US
dc.subjectEigenvalues and eigenfunctionsen_US
dc.subjectSignal processingen_US
dc.subjectThree dimensionalen_US
dc.titleMotion based clustering of model animations using PCAen_US
dc.title.alternativeModel animasyonlarının PCA kullanılarak harekete bağlı gruplandırılmasıen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage317en_US
dc.citation.epage320en_US
dc.identifier.doi10.1109/SIU.2009.5136396en_US
dc.publisherIEEEen_US


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