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.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/5571 |
| Date | 01 June 2003 |
| Creators | Louie, Jennifer |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 4307593 bytes, 5073756 bytes, application/postscript, application/pdf |
| Relation | AITR-2003-009, CBCL-227 |
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