Model the contents of a field as a Guassian distribution.
GaussianSurfaceModel computes the mean and covariance of the input,
optionally smoothing the model over time, and uses these values to evaluate
the probability density function over the field. High values in the output
indicate points that are highly consistent with the Guassian model. Values
close to zero indicate inconsistent points. This tends to pop out arbitrary
unusual objects on approximately homogeneous backgrounds.
The temporal smoothing parameter controls the rate at which the model is
permitted to change. With a value of 0, no smoothing is performed and a new
model is computed for every frame. Larger values mix a percentage of the
model from the previous frame. The smoothing parameter must be less than 1.
GaussianSurfaceModel supports both univariate and multivariate Gaussian
models. ScalarField input will result in a one-dimensional model.
VectorField input will produce a multivariate model. Note that the
multivariate probability density function is approximated using an LSQR
solver. This is more efficient, but also inexact. The multivariate version
of GaussianSurfaceModel is dramatically less efficient than the univariate
version. Use the univariate version whenever possible.
Model the contents of a field as a Guassian distribution.
GaussianSurfaceModel computes the mean and covariance of the input, optionally smoothing the model over time, and uses these values to evaluate the probability density function over the field. High values in the output indicate points that are highly consistent with the Guassian model. Values close to zero indicate inconsistent points. This tends to pop out arbitrary unusual objects on approximately homogeneous backgrounds.
The temporal smoothing parameter controls the rate at which the model is permitted to change. With a value of 0, no smoothing is performed and a new model is computed for every frame. Larger values mix a percentage of the model from the previous frame. The smoothing parameter must be less than 1.
GaussianSurfaceModel supports both univariate and multivariate Gaussian models. ScalarField input will result in a one-dimensional model. VectorField input will produce a multivariate model. Note that the multivariate probability density function is approximated using an LSQR solver. This is more efficient, but also inexact. The multivariate version of GaussianSurfaceModel is dramatically less efficient than the univariate version. Use the univariate version whenever possible.