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4 October 1999 Detection and clutter rejection in image sequences based on multivariate conditional probability
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A method of detecting dim targets in highly-cluttered time- varying image sequences is presented, where reliable clutter rejection is achieved by calibrating the multivariate statistics of a small number of generic space-time filters. The targets have sufficiently low SCR that a track-before- detect method is required. For targets where there is little prior information on velocity, a large number of filters is generally required to achieve a high response relative to the background. In the method described here, instead of applying thresholds to individual filters, joint filter statistics are used to estimate conditional threshold exceedance probabilities. A smaller number of more generic filters are applied, which are not finely tuned to targets but which characterize aspects of both targets and clutter. Potential targets are cued based on a non-parametric estimate of the probability of occurrence of similar clutter. Constant false alarm rates are inherent in the method. The method is demonstrated on examples of real forward-looking imagery of the sea surface, where glint is a source of strong clutter. Dim targets are distinguished form clutter by using the joint statistics of three variables: a constant-intensity blob filter, a filter tuned to sea glint flashes, and the vertical image coordinate.
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Graham H. Watson and Sharon K. Watson "Detection and clutter rejection in image sequences based on multivariate conditional probability", Proc. SPIE 3809, Signal and Data Processing of Small Targets 1999, (4 October 1999);

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