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We use statistical modeling approach to build the background model.

In [78], a simple bimodal model (normal distribution plus an unmodeled token) is used to build the background model.

Therefore, regulating the training set or the learning rate used to build the background model, it is possible to adjust how fast new static objects get incorporated into the background model.

This approximation can be justified by the large quantity of data available to build the background model (as opposed to the foreground models), and the correspondingly low estimator variance.

The software can be used to first build the background model corresponding to a ChIP-seq dataset, and then estimate the purified binding signal for a user-given set of genomic intervals (e.g., peaks).

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After the background model is built, we subtract the background model from the current frame and get a difference image.

The background model is built as a mixture of Gaussians.

After the background model is built, a binarization image from the difference image is needed to show the target.

The background model is built using four values: the mean, the standard deviation, the variation of the brightness, and chrominance distortion.

The background model for MEME was built using a Markov model with the 'null' sequences for translational targets (M1) and with the CLIP-tags in null mRNAs and in TTR + CLIP+ mRNAs outside the CLIP-tags for the direct translational targets (CM).

This advantage allows us to build a background model of relatively few samples, since the classifier is not very sensitive to the robustness of the background model.

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