The problem of detecting objects in noisy backgrounds is addressed. We derive detection filters by training a linear classifier, using features obtained from subimages corresponding to circular channels in the Fourier domain. The classifier weights approach the prewhitening matched filter when the classifier is trained for the detection of known objects in stationary noise. A simple form of rotation invariance is attained for considerably less computation than by the direct application of multiple matched filters. The method is demonstrated for the task of detecting simulated tumors in simulated nuclear medical images.
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