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.

Citation Download Citation

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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