Mexican hat wavelet

$\psi(t) = {2 \over {\sqrt {3\sigma}\pi^{1 \over 4}}} \left( 1 - {t^2 \over \sigma^2} \right) e^{-t^2 \over 2\sigma^2}$
The multidimensional generalization of this wavelet is called the Laplacian of Gaussian function. In practice, this wavelet is sometimes approximated by the difference of Gaussians function, because it is separable[citation needed] and can therefore save considerable computation time in two or more dimensions. The scale normalised Laplacian (in $L_1$-norm) is frequently used as a blob detector and for automatic scale selection in computer vision applications; see Laplacian of Gaussian and scale space. The Mexican hat wavelet can also be approximated by derivatives of Cardinal B-Splines[2]