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In order to accurately model the background, a dense disparity map needs to be computed.
We used N=20 samples (Eq. (1)) to model the background at each pixel location.
We model the background using Gaussian mixture models for each pixel.
In dynamic background regions, it is difficult to accurately model the background in the conventional KDE method.
It may be necessary to include assimilative techniques from real-time observations to reliably model the background conditions.
Huang and Chen [12] employed Gaussian mixture model to learn the color features and to model the background appearance variations under cast shadows.
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Figure 3 Noise modeling using ICA: (left) the n background images taken as input for ICA model; (right) the two outputs of the model are the background model and the noise model.
To estimate the extent to which the expression of an off-target transcript explains the matching probe intensity, we model the background-corrected, normalized probe intensity, y ij, of probe j in sample i, as (1) motivated by (Li and Wong, 2001).
Similar to GrabCut, we iteratively update the foreground model and the background model to accurately segment the common objects.
Hence, we improve the iteration method by simultaneously updating the foreground model and the background model of two images.
The common objects are finally segmented by mutually updating the foreground model and the background model of two images.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com