7 July 2000 Likelihood-based dual-threshold selection for imaging target trackers
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Most greylevel threshold-selection algorithms find thresholds that are optimal according to specific regional or global statistics. These traditional approaches do not involve any model of the target except that it is expected to be separable from the background by a threshold suite. Thus, they fail to make use of the most important feature of imaging target trackers: the well-known location of the target in each frame. We present a technique that uses knowledge of the target location to build up a temporally- smoothed greylevel distribution map from which we extract two thresholds that separate from the background the greylevels with a high probability of belonging to the target under track.
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Larisa Stephan, Larisa Stephan, Gillian K. Groves, Gillian K. Groves, "Likelihood-based dual-threshold selection for imaging target trackers", Proc. SPIE 4025, Acquisition, Tracking, and Pointing XIV, (7 July 2000); doi: 10.1117/12.391659; https://doi.org/10.1117/12.391659

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