One dimensional sieve introduction: Difference between revisions
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|[[Image:GaussianSmoothedSigma2.png|350px|Gaussian filtered]] | |[[Image:GaussianSmoothedSigma2.png|350px|Gaussian filtered]] | ||
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h=fspecial('Gaussian',9,2); | |||
Y=conv(X,(h(5,:)/sum(h(5,:))),'same'); | |||
====Non-linear==== | ====Non-linear==== | ||
{| border="0" cellpadding="5" cellspacing="5" | {| border="0" cellpadding="5" cellspacing="5" |
Revision as of 10:08, 15 November 2013
1D Signals
Matlab function IllustrateSIV_1 illustrates how MSERs (maximally stable extremal regions) and sieves are related. We start with one dimensional signals before moving to two dimensional images and three dimensional volumes.
Consider a signal, <math>X</math> X=getData('PULSES3WIDE') |
Filter
Linear
A linear Gaussian filter with <math>\sigma=2</math> attenuates extrema without introducing new ones. But blurring may be a problem. |
h=fspecial('Gaussian',9,2); Y=conv(X,(h(5,:)/sum(h(5,:))),'same');