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In this way, they were able to control how fast static objects get absorbed by the background models and detect them as those groups of pixels classified as background by the short-term but not by the long-term background model.
Notably, however, these values were often markedly higher than those suggested by the background models of the algorithms, supporting the earlier observation that especially the Poisson-based randomization model can severely underestimate the FDRs [ 22].
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After controlling for background variables, the curriculum variables explained an additional 2.4%% of the variance in latent QLT scores relative to the variance explained by the Background Model.
Compared with the amount of variance in QLT scores explained by the Background Model, the amount of additional variance explained by the Curriculum Model was relatively low, and its practical significance should be considered with caution.
This state can be reached as well by pixels belonging to the background scene being affected by spurious noise not characterized by the background model, (2) (PAP), partially absorbed pixel,.
The combination of the foreground masks obtained from the subtraction of two background models was already used by [6] in order to quickly adapt to changes in the scene while preventing foreground objects from being absorbed too fast by the background model.
The confidence value assigned to each observation sequence, Conf O), depends on: (1) the probability assigned by the mine model (λ m ), Pr(O|λ m ); (2) the probability assigned by the background model (λ c ), Pr(O|λ c ); and (3) the optimal state sequence.
The confidence value assigned to each observation sequence, Conf O), depends on: (1) the probability assigned by the mine model, P r(O|λ m ); (2) the probability assigned by the background model, P r(O|λ c ); and (3) the optimal state sequence.
The null hypothesis is that the cluster is obtained from random sequences generated by the background model.
Similarly to the RSAT output, the results are provided to the final user as tables with a 'weight score' column measured by the 'Background model estimation method' (17).
The E-value estimates the number of motifs with the same width and number of occurrence that would have equal or higher likelihood in the same number of random sequences generated by the background model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com