A Biologically-Inspired Neural Network Architecture for Image Processing
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Authors
Lazofson, Laurence E.
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Date of Issue
1990-12
Date
December 1990
Publisher
Language
en_US
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.
Type
Thesis
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Distribution Statement
Approved for public release; distribution is unlimited.
Approved for public release; distribution is unlimited.
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.