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Proceedings Paper

Psri Target Recognition In Range Imagery Using Neural Networks
Author(s): S. E. Troxel; S. K. Rogers; M. Kabrisky; J. P. Mills
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Paper Abstract

A method for classifying objects invariant to position, rotation, or scale is presented. Objects to be classified were multifunction laser radar data of tanks and trucks at various aspect angles. A segmented doppler image was used to mask the range image into candidate targets. Each target is then compared to stored templates representing the different classes. The template and the image were transformed into the magnitude of the Fourier transform with log radial and angle axis, lF (Ln r , 0)1, feature space. The classification is accomplished using the shape of the correlation peak of the IF (Ln r , 0)1 planes of an image and a template. A neural network was used to perform the classification with a classification accuracy near 100%. The neural network used in this study was a multilayer perception using a back propagation algorithm.

Paper Details

Date Published: 22 August 1988
PDF: 7 pages
Proc. SPIE 0938, Digital and Optical Shape Representation and Pattern Recognition, (22 August 1988); doi: 10.1117/12.976605
Show Author Affiliations
S. E. Troxel, Air Force Institute of Technology (United States)
S. K. Rogers, Air Force Institute of Technology (United States)
M. Kabrisky, Air Force Institute of Technology (United States)
J. P. Mills, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 0938:
Digital and Optical Shape Representation and Pattern Recognition
Richard D. Juday, Editor(s)

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