Steve-
Sorry, that should say ... you're not actually measuring the movement of a
particle between *successive* images.
-Jeff
------------------ Original Message ------------------
Subject: Re: [matlab] Time Delay Measurement in MATLAB
From: "Jeff Brower"
Date: Mon, August 17, 2009 6:07 pm
To: "Steve"
Cc: m...
-------------------
Steve-
> I am only cross correlating two windows of the
images, mainly the edges. The windows sense the pixel changes so it
can track lighter particles (due to gaps between red blood cells) moving
through both windows, and the cross
correlation should measure the time it takes to go from one window to the
next.
"...the cross correlation should measure the time it takes to go from one window
to the next."
At first glance, this sounds like it explains why you always get the number of
frames as a delay value. You're not
actually measuring the movement of a particle within one image.
-Jeff
> On Mon, Aug 17, 2009 at 8:39 AM, Jeff Brower
wrote:
>
>> Steve-
>>
>> Are you cross correlating the entire image, or only the extracted particle
(or other marker) from the fluid flow? If
>> the entire image, then are there multiple particles? If so should they all
be moving at the same speed?
>>
>> One way to test your code would be to create simulated images; for example,
place a pattern (circle, etc) in the
>> center of image 1, then move it slightly over and down in image 2, then run
xcorr(). You should get an x-y offset in
>> pixels.
>>
>> -Jeff
>>
>> > I am trying to measure fluid velocity in an AVI file, and am doing this by
converting each frame to an image and
measuring the mean intensity of the pixels in two different windows
spaced out. Now that I have two different
signals
>> > that should be theoretically time delayed, I need
to cross
>> > correlate them to see the time delay, but I am having trouble with xcorr. I
seem to obtain the signals alright,
but the xcorr always says that the delay is the number of frames I test
(when t=1:30, the delay is 30, when
t=1:400, the delay is 400). My code is posted
>> below:
>> >
>> > %Splits frames of AVI into grayscale images. Then it takes pixel
>> intensity
>> > %averages of the windows and inputs into two arrays AllIntensity1 and
%AllIntensity2. Then it cross correlates
the signals.
>> >
>> > A=mmreader('new.avi') %Reads Avi movie, places into A.mov for
Matlab's
>> %viewing pleasure.
>> >
>> > for t=1:30
>> > im = read( A, t);%reads sequential frames
>> > imgray=rgb2gray(im);%converts read images into grayscale
>> >
>> > %Chooses windows in first iteration only
>> > if (t==1)
>> > BW1=roipoly(imgray);
>> > BW2=roipoly(imgray);
>> > end
>> > %computes mean intensity for each window separately, placing in two
>> %arrays
>> > Stats=regionprops(BW1,imgray,'MeanIntensity');
>> > AllIntensity1(t)=Stats.MeanIntensity;
>> > Stats=regionprops(BW2,imgray,'MeanIntensity');
>> > AllIntensity2(t)=Stats.MeanIntensity;
>> >
>> > end
>> > AllIntensity2=[100 100 103 105 103 100 AllIntensity1(1:24)];
>> >
>> > %%%%%%%%%%%%%%%%%%%%%%%%%%%Cross Correlation
>> Begins%%%%%%%%%%%%%%%%%%%%%%%%
>> xc=xcorr(AllIntensity1,AllIntensity2,'coeff');
>> > [a,delay]=max(xc);
>> > a
>> > delay
>> > subplot(311),plot(AllIntensity1)
>> > subplot(312),plot(AllIntensity2)
>> > subplot(313),plot(xc(1:59))
>> >
>> > Any Ideas?? Thanks!
>> > Steve
