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dc.contributor.authorAr, İlktan
dc.contributor.authorAkgül, Yusuf Sinan
dc.date.accessioned2019-06-27T08:04:16Z
dc.date.available2019-06-27T08:04:16Z
dc.date.issued2012
dc.identifier.isbn9783642335648
dc.identifier.isbn9783642335631
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/20.500.12469/918
dc.identifier.urihttps://doi.org/10.1007/978-3-642-33564-8_32
dc.description.abstractThis paper proposes a novel approach to recognize actions and objects within the context of each other. Assuming that the different actions involve different objects in image sequences and there is one-to-one relation between object and action type we present a Bayesian network based framework which combines motion patterns and object usage information to recognize actions/objects. More specifically our approach recognizes high-level actions and the related objects without any body-part segmentation hand tracking and temporal segmentation methods. Additionally we present a novel motion representation based on 3D Haar-like features which can be formed by depth color or both images. Our approach is also appropriate for object and action recognition where the involved object is partially or fully occluded. Finally experiments show that our approach improves the accuracy of both action and object recognition significantly.
dc.language.isoEnglish
dc.publisherSpringer-Verlag Berlin
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAction and object recognition
dc.subjectBayesian network
dc.subjectMotion pattern
dc.titleA framework for combined recognition of actions and objects
dc.typeProceedings Paper
dc.identifier.startpage264
dc.identifier.endpage271
dc.relation.journalInternational Conference on Computer Vision and Graphics
dc.identifier.volume7594
dc.identifier.wosWOS:000313005700032
dc.identifier.doi10.1007/978-3-642-33564-8_32
dc.contributor.khasauthorAr, İlktan


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