Year: 2003
Type: dissertation
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.
Author: Jennifer Louie
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Collaborative Research: CRCNS: Detection and Recognition of Objects in Visual Cortex, ITR/SY: A Neuromorphic Vision System for Every-Citizen Interfaces, Trainable Visual Aids for Object Detection and Identification, ITR: From Bits to Information: Statistical Learning Technologies for Digital Information Management and Search, CRCNS: Bioinformatics of Alternative Splicing in the Nervous System, G8876996369
OpenAlex ID: W2142914342