2026-09-19T02:03:13Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/296782022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Tomaso Poggio.
Louie, Jennifer, 1980-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2006-03-24T16:14:26Z
2006-03-24T16:14:26Z
2003
2003
http://hdl.handle.net/1721.1/29678
53834027
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (leaves 67-68).
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.
by Jennifer Louie.
M.Eng.
68 leaves
2470898 bytes
2470707 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
A biological model of object recognition with feature learning
Thesis
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