Detection of degraded target signatures: statistical versus neural networks

James A. Robertson; Steven W. Worrell; Dave O'Quinn; Alain Mozart Charles

doi:10.1117/12.139980

16 September 1992 Detection of degraded target signatures: statistical versus neural networks

James A. Robertson, Steven W. Worrell, Dave O'Quinn, Alain Mozart Charles

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.139980
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

Pattern recognition applications require algorithms be optimized to provide accurate and reproducible target identification. Approaches usually incorporate a combination preprocessing, feature extraction, and classification algorithms whose parameters have been adjusted for the best performance against a particular set of images. With the variety of neural network and statistical techniques available at each of these processing steps, choosing the correct algorithms for a particular application may be difficult. A Pattern Recognition Workstation (PRW) has been developed to assist in the selection of these algorithms. The workstation provides a variety of image degradation techniques to assist the user in assessing the performance of algorithms as a function of obscuration, noise levels, scale and rotation. Initial results are reported from preprocessors including the Contrast-Orientation-Ratio- Threshold-Maximum (CORT-X), Sobel and Laplacian, feature extractors including the Gabor Transform, Invariant Moments, and Fourier-Log-Polar Transform, and classifiers including Backpropagation and Bayes decision theory. The resulting class decision statistics are presented to assess robustness with respect to obscuration and noise levels.

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James A. Robertson, Steven W. Worrell, Dave O'Quinn, and Alain Mozart Charles "Detection of degraded target signatures: statistical versus neural networks", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.139980

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KEYWORDS

Neural networks

Detection and tracking algorithms

Algorithm development

Image processing

Feature extraction

Target detection

Artificial neural networks

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