Date of Award

Fall 2018

Project Type

Thesis

Program or Major

Electrical and Computer Engineering

Degree Name

Master of Science

First Advisor

Richard A Messner

Second Advisor

Michael J Carter

Third Advisor

Wayne J Smith

Abstract

While significant strides in neural network and machine vision applications have been made in recent years, humans still remain the most proficient at feature extraction and pattern recognition tasks. Some researchers have attempted to utilize select aspects of the human visual system in order to perform application-specific visual tasks. However, none have been able to develop a computational model of the biological human visual system that can perform the many complex pattern recognition tasks that we do as humans. This thesis focuses on significant improvements to an existing human visual system model created by N. Radhi, and the novel implementation of a deep learning system for road detection utilizing non-uniformly sampled images in log-polar coordinate space. A convolutional neural network is used to compare the non-uniformly sampled image model with the conventional uniform structure, with the non-uniform model demonstrating significant increases in processing speed while retaining high validation accuracy. Comparisons between the uniform and non-uniform models when subjected to a variety of preprocessing methods are presented.

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