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Second, a background model based on GMM (GMM-UBM) of M mixture components is trained using data from all classes.
Chen et al. [6] suggested a hierarchical background model based on the fact that the background images consist of different objects whose conditions may change frequently.
For motif discovery in Brassicaceae, we used a background model based on Arabidopsis promoters, for Fabaceae we used a background model based on soybean promoters, and for motif discovery in Poaceae we used a background model based on rice promoters.
A background model based on local minima was used by Tyson et al. (1986).
We used a background model based on the entire set of intergenic regions (3396 sequences) in C. parvum to train these algorithms.
The thermodynamic stability of pre-miRNAs is examined as previously reported [ 21], but also used a background model based on the random and shuffled pseudo pre-miRNAs.
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In [104], an algorithm for background modeling based on spatiotemporal patches especially suited for night outdoor scenes is presented.
Using Parzen density estimation and foreground object detection, a fast estimation method was presented[22] and an automatic background modeling based on multivariate non-parametric KDE was proposed[23].
BE-AAPSA is based on a previously developed system, where two adaptive background models based on weight arrays with temporal learning mechanism identify dynamic objects within a video scene.
Nonrecursive approaches maintain a buffer with a certain quantity of previous video frames and estimate a background model based solely on the statistical properties of these frames.
All three peak-callers can either use statistical model as background or generate a specific model based on experimental data.
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