From aac248a6c389a1c34160d4c58948d16595c54d95 Mon Sep 17 00:00:00 2001 From: toni Date: Wed, 14 Feb 2018 19:37:39 +0100 Subject: [PATCH] eulaaaar --- tex/chapters/mvg.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tex/chapters/mvg.tex b/tex/chapters/mvg.tex index 94cad5f..ac7b1a9 100644 --- a/tex/chapters/mvg.tex +++ b/tex/chapters/mvg.tex @@ -16,7 +16,7 @@ where $\sigma$ is a smoothing parameter called standard deviation. %In the discrete case the Gaussian filter is easily computed with the sliding window algorithm in time domain. If the filter kernel is separable, the convolution is also separable i.e. multi-dimensional convolution can be computed as individual one-dimensional convolutions with a one-dimensional kernel. -Because of $\operatorname{e}^{x^2+y^2} = \operatorname{e}^{x^2}\cdot\operatorname{e}^{y^2}$ the Gaussian filter is separable and can be easily applied to multi-dimensional signals. \todo{quelle} +Because of $e^{x^2+y^2} = e^{x^2}\cdot e^{y^2}$ the Gaussian filter is separable and can be easily applied to multi-dimensional signals. \todo{quelle} % TODO ähnlichkeit Gauss und KDE -> schneller Gaus = schnelle KDE