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This factoring relies on a multi-level modelling scheme where a universal background model can be successively specialized to environment, speaker, and acoustic units.
The photon flux of an unconvolved background model can be integrated by supplying the corresponding Sherpa-assigned background ID as the argument (e.g. "1" or "2" for the first or second background data set automatically located by Sherpa in the corresponding source data set).
The counts of a convolved background model can be summed by supplying the corresponding Sherpa-assigned background ID as the argument (e.g. "1" or "2" for the first or second background data set automatically located by Sherpa in the corresponding source data set).
The amplitudes of an unconvolved background model can be summed by supplying the corresponding Sherpa-assigned background ID as the argument (e.g. "1" or "2" for the first or second background data set automatically located by Sherpa in the corresponding source data set).
The probability value produced by the mine (background) model can be thought of as an estimate of the probability of the observation sequence given that there is a mine (background) present.
In the CAVIAR scene LeftBag, static objects are removed that early, that every background model can be tuned not to absorb them without risking the responsiveness of the background model.
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A study of some well-known background models can be found in [8 10] and references therein.
Then, the computation and storage loads on background modelling can be significantly reduced.
Background models can be provided as frequencies of codons and three (for Homo sapiens, Saccharomyces cerevisiae and Escherichia coli) are distributed with CodonLogo.
Background on this model can be found in Wood [ 36].
From such a background, the present model can be regarded as a local linearization applied in the tangent plane of the spherical camera model at the center of the visual sensor.
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