Sentence examples for modeling the background from inspiring English sources

The Weibull distribution has been used for modeling the background of the image.

The foreground likelihood maps in each view is estimated by modeling the background using a mixture of Gaussians.

Camouflage effects derive from the similarity between the features that characterize the foreground and those used for modeling the background.

Many popular background subtraction algorithms operate by modeling the background with a probability density function (pdf) at each pixel.

In [82], a method for modeling the background that uses per-pixel, time-adaptive, Gaussian mixtures in the combined input space of depth and luminance-invariant color is proposed.

As a consequence, the Weibull distribution, which lies between the two extremes of log-normal and Rayleigh, appears to be a good choice for modeling the background in multi-beam sonar image.

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.

In dynamic background regions, it is difficult to accurately model the background in the conventional KDE method.

The background model used is based on Elgammal and Duraiswami's work [33] that use kernel density estimation to model the background.

It may be necessary to include assimilative techniques from real-time observations to reliably model the background conditions.