One dimensional sieve introduction: Difference between revisions
Jump to navigation
Jump to search
Line 2: | Line 2: | ||
=<span style="color:Chocolate">1D Signals</span>= | =<span style="color:Chocolate">1D Signals</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" | {| border="0" cellpadding="5" cellspacing="5" 3D extrema thumb.gif | ||
|- valign="top" | |- valign="top" | ||
|width="20%"| [[Image: | |width="20%"| [[Image:IllustrateSIV_1_01_thumb.gif|140px|AAMToolbox]] | ||
|'''Consider a signal''', <math>X</math><br> | |'''Consider a signal''', <math>X</math><br> | ||
X=getData('PULSES3WIDE') | X=getData('PULSES3WIDE') |
Revision as of 10:16, 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') >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');