Date of Award

12-1990

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Department of Electrical and Computer Engineering

First Advisor

Steven K. Rogers, PhD

Abstract

This thesis project included a literature survey of biological and artificial neural network research followed by development and testing of high- order and image recognition hierarchical neural network algorithms. Following training, performance testing of second-order and third-order networks yielded maximum accuracies comparable to those achieved by multilayer perceptron classifiers operating on test data sets. Several versions of an image classification algorithm were tested for learning performance using pixel data from forward-looking infrared (FLIR) images of tanks, trucks, target boards, and clutter. Employing the biologically-motivated Lambertization and contrast normalization of pixel windows, correlations with multiple Gabor function wavelets, and a 'phase synchronizing' local averaging routine, the image classification network extracted data features. Different network versions fed the extracted features to varying output classification schemes. To improve separation of problem classes, recommendations were made for varying the parameters of the Gabor function wavelets and modifying the phase synchronization scheme to extract more suitable features from image pixel data.

AFIT Designator

AFIT-GEO-ENG-90D-04

DTIC Accession Number

ADA230495

Comments

The author's Vita page is omitted

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