| dc.contributor.advisor | Tomaso Poggio. | en_US |
| dc.contributor.author | Louie, Jennifer, 1980- | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2006-03-24T16:14:26Z | |
| dc.date.available | 2006-03-24T16:14:26Z | |
| dc.date.copyright | 2003 | en_US |
| dc.date.issued | 2003 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/29678 | |
| dc.description | Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003. | en_US |
| dc.description | Includes bibliographical references (leaves 67-68). | en_US |
| dc.description.abstract | 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. | en_US |
| dc.description.statementofresponsibility | by Jennifer Louie. | en_US |
| dc.format.extent | 68 leaves | en_US |
| dc.format.extent | 2470898 bytes | |
| dc.format.extent | 2470707 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | A biological model of object recognition with feature learning | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | M.Eng. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| dc.identifier.oclc | 53834027 | en_US |
