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- W1992192635 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." @default.
- W1992192635 created "2016-06-24" @default.
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- W1992192635 date "1992-09-16" @default.
- W1992192635 modified "2023-09-27" @default.
- W1992192635 title "Detection of degraded target signatures: statistical versus neural networks" @default.
- W1992192635 doi "https://doi.org/10.1117/12.139980" @default.
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