This function divides the MAT matrix in windows of WLines lines and WColumns
columns, then in each one of these windows it is calculated the mean value of all
elements.
This function implements the temporal speckle contrast matrix [1], the temporal
speckle standard deviation matrix and the temporal speckle mean matrix.
This function implements the Absolute Value of the Differences (AVD) method [1],
only using a pixel-by time, with the normalization of the co-occurrence matrix (COM)
proposed by CARDOSO, R
This function implements the Inertia Moment (IM) [1] method, only on a pixel-by time,
with the normalization of the co-occurrence matrix (COM) proposed by
CARDOSO, R.R.
This function implements the Regular Value of the Differences method [1],
only on a pixel-by time, with the normalization of the co-occurrence matrix (COM)
proposed by CARDOSO, R.R.
This function implements the Motion History Image (MHI) technique [1-2], and considers a pixel as in activity, where it should have an absolute intensity jump superior to U.
This function creates the THSP (Time History Speckle Pattern)[1][2]
of a set of M points (pixels) randomly (Gaussian) selected in EXAMPLE_MATRIX,
and through DATA(:,:,k) for all k value.
This function creates the THSP (Time History Speckle Pattern)[1][2] of a set
of pixels in a line in a data pack (DATA), (This function is an alias of
thsp() function).
This function creates the THSP (Time History Speckle Pattern)[1][2] of a set
of M points (pixels) randomly (Uniform) selected in DATA(:,:,1), and through
DATA(:,:,k) for all k value.
This function divides the MAT matrix in windows of WLines lines and WColumns columns, then in each one of these windows it is calculated the mean value of all elements.