public interface BackgroundAlgorithmGaussian
Performs background subtraction on a moving image by modeling each pixel as an independent Guassian distribution. For computational efficiency each band is modeled as having a diagonal covariance matrix with off diagonal terms set to zero. This model is susceptible to
By assuming that off diagonal terms are zero that's the same as assuming that all bands are independent distribution. This is an approximation but according to several papers it doesn't hurt performance much but simplifies computations significantly.
Internally the mean and variance are stored in ImageFloat32 images. This allows for the mean and variance of each pixel to be interpolated, reducing artifacts along the border of objects.
Tuning Parameters:
| Modifier and Type | Method and Description |
|---|---|
float |
getInitialVariance()
Returns the initial variance assigned to a pixel
|
float |
getLearnRate()
Returns the learning rate.
|
float |
getMinimumDifference() |
float |
getThreshold() |
void |
setInitialVariance(float initialVariance)
Sets the initial variance assigned to a pixel
|
void |
setLearnRate(float learnRate)
Specifies the learning rate
|
void |
setMinimumDifference(float minimumDifference) |
void |
setThreshold(float threshold) |
float getInitialVariance()
void setInitialVariance(float initialVariance)
initialVariance - initial variancefloat getLearnRate()
void setLearnRate(float learnRate)
learnRate - 0 (slow) to 1 (fast)float getThreshold()
void setThreshold(float threshold)
float getMinimumDifference()
void setMinimumDifference(float minimumDifference)