Abstract
Matched filtering methods are often used to detect objects in images. However, for an image scene consisting of many different patterns which are corrupted by noise, direct use of such matched filters would be very time-consuming, and the performance of the matched filter is suboptimal if the noise is nonwhite. In these cases more complex filtering processes are required which are based on the statistical characteristics of the images involved.
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