A Biological Model of Object Recognition with Feature Learning

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/5571

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DC Field	Value	Language
dc.creator	Louie, Jennifer	-
dc.date	2004-10-01T14:00:10Z	-
dc.date	2004-10-01T14:00:10Z	-
dc.date	2003-06-01	-
dc.date.accessioned	2013-10-09T02:40:11Z	-
dc.date.available	2013-10-09T02:40:11Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AITR-2003-009	-
dc.identifier	CBCL-227	-
dc.identifier	http://hdl.handle.net/1721.1/5571	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Previous 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.	-
dc.format	4307593 bytes	-
dc.format	5073756 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AITR-2003-009	-
dc.relation	CBCL-227	-
dc.subject	AI	-
dc.title	A Biological Model of Object Recognition with Feature Learning	-
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