
pmid: 15875803
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.
Stochastic Processes, Models, Statistical, Information Storage and Retrieval, Reproducibility of Results, Image Enhancement, Sensitivity and Specificity, Pattern Recognition, Automated, Imaging, Three-Dimensional, Artificial Intelligence, Subtraction Technique, Image Interpretation, Computer-Assisted, Cluster Analysis, Computer Simulation, Algorithms
Stochastic Processes, Models, Statistical, Information Storage and Retrieval, Reproducibility of Results, Image Enhancement, Sensitivity and Specificity, Pattern Recognition, Automated, Imaging, Three-Dimensional, Artificial Intelligence, Subtraction Technique, Image Interpretation, Computer-Assisted, Cluster Analysis, Computer Simulation, Algorithms
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