Optimal detection of a target with random gray levels on a spatially disjoint background noise.
We describe a pattern-recognition processor that is optimal for detection and location of a target with white Gaussian random gray levels on a white random spatially disjoint background. We show that this algorithm consists of correlations of the silhouette of the reference object with preprocessed versions of the scene image. This result can provide a theoretical basis for pattern-recognition techniques that use nonlinear preprocessing of images before correlation.
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