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Such background estimation and adaptation system discriminates interesting foreground objects from the uninteresting background by building the background model of the image [15, 16].
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These scene-change transition problems can be avoided by building the background subtraction models that do not depend on the intensity component of the image [18], which unfortunately is still in the experimental phase.
This formed the basis of the background model.
The background model is a regression based low rank model.
The background model is represented by (7).
how the background model is represented?
Stage 1: Building the grey model.
Model validation deals with building the right model.
Building the kinetic model: RDL and CR.
After building the reference background, we need to extract a significant keypoint from the reference image.
In building the models we have assumed that the true τ is known and have defined it as the effect of a single risk locus in the background of the average number of risk loci.
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