One dimensional sieve introduction: Difference between revisions
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[http://cmpdartsvr3.cmp.uea.ac.uk/wiki/BanghamLab/index.php/MSER%27s_and_Connected_sets#One_dimensional_signals Return to MSERs and extrema]<br><br> | [http://cmpdartsvr3.cmp.uea.ac.uk/wiki/BanghamLab/index.php/MSER%27s_and_Connected_sets#One_dimensional_signals Return to MSERs and extrema]<br><br> | ||
=<span style="color:Chocolate">1D Signals</span>= | =<span style="color:Chocolate">1D Signals to MSERs and granules</span>= | ||
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. | 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. | ||
{| border="0" cellpadding="5" cellspacing="5" 3D extrema thumb.gif | {| border="0" cellpadding="5" cellspacing="5" 3D extrema thumb.gif |
Revision as of 10:17, 15 November 2013
1D Signals to MSERs and granules
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') >blue X=0 5 5 0 0 1 1 4 3 3 2 2 1 2 2 2 1 0 0 0 1 1 0 3 2 0 0 0 6 0 0 |
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');
Non-linear
scaleA=1; Y1=SIVND_m(X,scaleA,'o');
scaleB=2; Y2=SIVND_m(X,scaleB,'o');