Abstract
Matched filters are often used to detect image objects. However, for an image scene consisting of many different patterns corrupted by noise, the direct use of matched filters is time consuming and the performance of the matched filter deteriorates if the noise is not stationary and white. We develop a hierarchical approach for the detection of multiple objects which is divided into three steps: (a) prefiltering; (b) pattern recognition; and (c) object detection. This approach reduces computation time by more than 50% and increases classification efficiency compared with the direct matched filtering approach.
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