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Motion parameters computed from frozen frames must be discarded before to continue with the estimation process.
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Then, we continue with the motion estimation for each of the N best candidates found in the frame F(t - 1) by finding their N best matches in the frame F(t - 2).
The discussion henceforth continues with the DDD estimation results which, I believe, are more reliable as this strategy enables the elimination of the potential confounding factors.
Highlighting the importance of Cramer-Rao bound not only as a means to quantify errors from a set of parameters to be estimated but also as a modeling tool since it allows for the evaluation of the impact of various parameters in the estimation error, we will continue with the derivation of this bound for our model, introduced mainly in (17), (19), and (30).
Then, continue with the grooming.
At the back, continue with the tail.
Audacity continues with the desserts.
The stage set continues with the appetizers.
The algorithm continues with the next row.
FastWISE continues with the iteration phase.
The coalition has continued with the plan.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com