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In [14], the authors tracked multiple flying targets using a Multiple Hypothesis Tracker (MHT).
The multiple hypothesis tracker (MHT) can establish the hypotheses of all possible measurements and keep these hypotheses [4].
The multiple hypothesis tracker (MHT) [2] uses a sequential probability ratio as a track quality measure for FTD.
One data association method suitable for the situation is the multiple hypothesis tracker (MHT) originally presented in[19].
Furthermore, in order to detect and position the defects which are not detected in all projections more efficiently, other trackers or modifications to the multiple hypothesis tracker used here could be interesting to explore.
In this article, a multiple hypothesis tracker with an extended Kalman filter is used to track an unknown number of pore indications in a sequence of radiographs as an object is rotated.
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The probabilistic multiple hypotheses tracker (PMHT) [10, 11] assumes that the data association is an independent process to overcome the problems with the pruning.
On the other hand, the multiple hypotheses tracker (MHT) [4, 5] attempts to compute all the possible associations along the time.
This issue, known as the data association problem, can be solved using the well-known multiple hypotheses tracker (MHT) [4] or the joint probabilistic data association filter (JPDAF) [5].
A similar threshold is given on a lower level, as we use a Multiple-Hypotheses-Tracker (MHT) [24] extension for the ASD filter [3].
All p-values reported are unadjusted for multiple hypothesis testing.
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