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dc.contributor.authorBhole, Chetan
dc.date.accessioned2016-04-05T16:19:12Z
dc.date.available2016-04-05T16:19:12Z
dc.date.issued2006
dc.identifier.isbn9780542772276
dc.identifier.other304946762
dc.identifier.urihttp://hdl.handle.net/10477/49604
dc.description.abstractIn this thesis we define and employ behavioral features along with shape features to characterize objects. We model the dynamic behavior of each object class by its common pattern of movement to help disambiguate different objects that may look similar but belong to different object classes in light of their distinct behavioral features. We show that for a simulated environment problem where we create a database of a mix of objects with small and large variations in shapes, sizes and behavior models, the error rate of the system that uses shape alone decreases from a top-choice error rate of 7-30% to a top-choice error rate of 2-3% using shape and behavioral features, with performance gain with the addition of behavioral features depending on the noise levels in cases where the shapes are similar but objects have different behaviors. (Abstract shortened by UMI.)
dc.languageEnglish
dc.sourceDissertations & Theses @ SUNY Buffalo,ProQuest Dissertations & Theses Global
dc.subjectApplied sciences
dc.titleObject recognition using shape and behavioral features
dc.typeDissertation/Thesis


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