Time–frequency representation: Difference between revisions

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[[File:MexicanHatMathematica.svg|thumb|250px|[[Mexican Hat wavelet|Mexican Hat]]]]
 
In [[mathematics]] and [[numerical analysis]], the '''Ricker wavelet'''<ref>http://209.91.124.56/publications/recorder/1994/09sep/sep94-choice-of-wavelets.pdf</ref>
:<math>\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}</math>
is the negative [[normalizing constant|normalized]] second [[derivative]] of a [[Gaussian function]], i.e., up to scale and normalization, the second [[Hermite function]]. It is a special case of the family of [[continuous wavelet]]s ([[wavelet]]s used in a [[continuous wavelet transform]]) known as [[Hermitian wavelet]]s. It is usually only referred to as the "Mexican hat" in the Americas, due to cultural association; see "[[sombrero]]". The Ricker Wavelet is frequently employed to model seismic data, and as a broad spectrum source term in computational electrodynamics.
 
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|date=April 2012}} and can therefore save considerable computation time in two or more dimensions. The scale normalised Laplacian (in <math>L_1</math>-norm) is frequently used as a [[blob detection|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 [[derivative]]s of [[B-spline#Cardinal_B-spline|Cardinal B-Splines]]<ref>Brinks R: ''On the convergence of derivatives of B-splines to derivatives of the Gaussian function'', Comp. Appl. Math., 27, 1, 2008</ref>
 
==References==
{{reflist}}
 
[[Category:Continuous wavelets]]

Revision as of 23:30, 7 November 2013

Mexican Hat

In mathematics and numerical analysis, the Ricker wavelet[1]

is the negative normalized second derivative of a Gaussian function, i.e., up to scale and normalization, the second Hermite function. It is a special case of the family of continuous wavelets (wavelets used in a continuous wavelet transform) known as Hermitian wavelets. It is usually only referred to as the "Mexican hat" in the Americas, due to cultural association; see "sombrero". The Ricker Wavelet is frequently employed to model seismic data, and as a broad spectrum source term in computational electrodynamics.

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 separablePotter or Ceramic Artist Truman Bedell from Rexton, has interests which include ceramics, best property developers in singapore developers in singapore and scrabble. Was especially enthused after visiting Alejandro de Humboldt National Park. and can therefore save considerable computation time in two or more dimensions. The scale normalised Laplacian (in -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]

References

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  1. http://209.91.124.56/publications/recorder/1994/09sep/sep94-choice-of-wavelets.pdf
  2. Brinks R: On the convergence of derivatives of B-splines to derivatives of the Gaussian function, Comp. Appl. Math., 27, 1, 2008