Abstract
One of the central problems in Automated Target Recognition is to accommodate the infinite variety of clutter in real military environments. The principle focus of our paper is on the construction of metric spaces where the metric measures the distance between objects of interest invariant to the infinite variety of clutter. Such metrics are formulated using second-order random field models. Our results indicate that this approach significantly improves detection/classification rates of targets in clutter.
Publication types
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Comparative Study
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Evaluation Study
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Research Support, U.S. Gov't, Non-P.H.S.
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Validation Study
MeSH terms
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Algorithms*
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Artificial Intelligence*
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Cluster Analysis
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Computer Simulation
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Image Enhancement / methods
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Image Interpretation, Computer-Assisted / methods*
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Imaging, Three-Dimensional / methods*
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Information Storage and Retrieval / methods
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Models, Statistical
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Pattern Recognition, Automated / methods*
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Reproducibility of Results
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Sensitivity and Specificity
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Stochastic Processes
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Subtraction Technique*