Advisor

Melanie Mitchell

Date of Award

Winter 2-20-2014

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.) in Computer Science

Department

Computer Science

Physical Description

1 online resource (xx, 131 pages)

Subjects

Computer vision, Neural networks (Computer science), Machine learning

DOI

10.15760/etd.1664

Abstract

I conduct a study of learning in HMAX-like models, which are hierarchical models of visual processing in biological vision systems. Such models compute a new representation for an image based on the similarity of image sub-parts to a number of specific patterns, called prototypes. Despite being a central piece of the overall model, the issue of choosing the best prototypes for a given task is still an open problem. I study this problem, and consider the best way to increase task performance while decreasing the computational costs of the model. This work broadens our understanding of HMAX and related hierarchical models as tools for theoretical neuroscience, while simultaneously increasing the utility of such models as applied computer vision systems.

Persistent Identifier

http://archives.pdx.edu/ds/psu/11083

Share

COinS