A Biological Model of Object Recognition with Feature Learning

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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