Likelihood-based dual-threshold selection for imaging target trackers

Larisa Stephan; Gillian K. Groves

doi:10.1117/12.391659

7 July 2000 Likelihood-based dual-threshold selection for imaging target trackers

Larisa Stephan, Gillian K. Groves

Proceedings Volume 4025, Acquisition, Tracking, and Pointing XIV; (2000) https://doi.org/10.1117/12.391659
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

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.

Citation Download Citation

Larisa Stephan and Gillian K. Groves "Likelihood-based dual-threshold selection for imaging target trackers", Proc. SPIE 4025, Acquisition, Tracking, and Pointing XIV, (7 July 2000); https://doi.org/10.1117/12.391659

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