Sponsor
Portland State University. Department of Electrical and Computer Engineering
First Advisor
Dan Hammerstrom
Date of Publication
Spring 1-1-2012
Document Type
Thesis
Degree Name
Master of Science (M.S.) in Electrical and Computer Engineering
Department
Electrical and Computer Engineering
Language
English
Subjects
Brain, Cortex, HMAX, Computational neuroscience, Robot vision, Pattern recognition systems, Image processing -- Digital techniques -- Computer programs, Optical character recognition devices
DOI
10.15760/etd.632
Physical Description
1 online resource (vii, 66 p.)
Abstract
This paper proposes an end-to-end, scale invariant, visual object recognition system, composed of computational components that mimic the cortex in the brain. The system uses a two stage process. The first stage is a filter that extracts scale invariant features from the visual field. The second stage uses inference based spacio-temporal analysis of these features to identify objects in the visual field. The proposed model combines Numenta's Hierarchical Temporal Memory (HTM), with HMAX developed by MIT's Brain and Cognitive Science Department. While these two biologically inspired paradigms are based on what is known about the visual cortex, HTM and HMAX tackle the overall object recognition problem from different directions. Image pyramid based methods like HMAX make explicit use of scale, but have no sense of time. HTM, on the other hand, only indirectly tackles scale, but makes explicit use of time. By combining HTM and HMAX, both scale and time are addressed. In this paper, I show that HTM and HMAX can be combined to make a com- plete cortex inspired object recognition model that explicitly uses both scale and time to recognize objects in temporal sequences of images. Additionally, through experimentation, I examine several variations of HMAX and its
Rights
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
Persistent Identifier
http://archives.pdx.edu/ds/psu/8316
Recommended Citation
Voils, Danny, "Scale Invariant Object Recognition Using Cortical Computational Models and a Robotic Platform" (2012). Dissertations and Theses. Paper 632.
https://doi.org/10.15760/etd.632
Included in
Artificial Intelligence and Robotics Commons, Electrical and Computer Engineering Commons
Comments
Portland State University. Dept. of Electrical and Computer Engineering