One dimensional sieve introduction: Difference between revisions
Jump to navigation
Jump to search
Line 26: | Line 26: | ||
Y=conv(X,(h(5,:)/sum(h(5,:))),'same'); | Y=conv(X,(h(5,:)/sum(h(5,:))),'same'); | ||
====Non-linear==== | ====Non-linear: the starting point for MSER's==== | ||
{| border="0" cellpadding="5" cellspacing="5" | {| border="0" cellpadding="5" cellspacing="5" | ||
|- valign="top" | |- valign="top" |
Revision as of 11:02, 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: the starting point for MSER's
scaleA=1; Y1=SIVND_m(X,scaleA,'o');
scaleB=2; Y2=SIVND_m(X,scaleB,'o');
red=double(X)-double(Y1); green=double(Y1)-double(Y2);