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Object Recognition with Pictorial Structures

dc.date.accessioned2004-10-20T20:28:15Z
dc.date.accessioned2018-11-24T10:22:58Z
dc.date.available2004-10-20T20:28:15Z
dc.date.available2018-11-24T10:22:58Z
dc.date.issued2001-05-01en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/7073
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/7073
dc.description.abstractThis thesis presents a statistical framework for object recognition. The framework is motivated by the pictorial structure models introduced by Fischler and Elschlager nearly 30 years ago. The basic idea is to model an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. The problem of detecting an object in an image and the problem of learning an object model using training examples are naturally formulated under a statistical approach. We present efficient algorithms to solve these problems in our framework. We demonstrate our techniques by training models to represent faces and human bodies. The models are then used to locate the corresponding objects in novel images.en_US
dc.format.extent15588217 bytes
dc.format.extent1282972 bytes
dc.language.isoen_US
dc.titleObject Recognition with Pictorial Structuresen_US


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