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A Biological Model of Object Recognition with Feature Learning

dc.date.accessioned2004-10-01T14:00:10Z
dc.date.accessioned2018-11-24T10:09:39Z
dc.date.available2004-10-01T14:00:10Z
dc.date.available2018-11-24T10:09:39Z
dc.date.issued2003-06-01en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/5571
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/5571
dc.description.abstractPrevious biological models of object recognition in cortex have been evaluated using idealized scenes and have hard-coded features, such as the HMAX model by Riesenhuber and Poggio [10]. Because HMAX uses the same set of features for all object classes, it does not perform well in the task of detecting a target object in clutter. This thesis presents a new model that integrates learning of object-specific features with the HMAX. The new model performs better than the standard HMAX and comparably to a computer vision system on face detection. Results from experimenting with unsupervised learning of features and the use of a biologically-plausible classifier are presented.en_US
dc.format.extent4307593 bytes
dc.format.extent5073756 bytes
dc.language.isoen_US
dc.subjectAIen_US
dc.titleA Biological Model of Object Recognition with Feature Learningen_US


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