Prev: use COM type library (.tlb) in matlab?
Next: GET PAID FOR WORKING ONLINE $100 ON YOUR FIRST DAY
From: Prashant sangulgi on 11 Aug 2010 08:22
Plz send the source code for pca based image fusion.... My work is pended due to this...
From: us on 11 Aug 2010 08:32
"Prashant sangulgi" <psangulgi(a)gmail.com> wrote in message <i3u4lc$34c$1(a)fred.mathworks.com>...
> Plz send the source code for pca based image fusion.... My work is pended due to this...

only a robot or a human being with severe cognitive deficiencies repeats posts over and over and over and over and..., again...
read up on what was told you before...
and - do NOT double post(!)...

us
From: Jan Simon on 11 Aug 2010 08:59
Dear Urs,

> only a robot or a human being with severe cognitive deficiencies repeats posts over and over and over and over and..., again...

Sorry, I cannot confirm this.
My son asked me repeatedly, why his shadow is longer than he is in the evening. I'm sure, it was not a cognitive deficiency, but more likely he loved how I pronounce the term "arcus tangens".

Kind regards, Jan
From: dpb on 11 Aug 2010 09:39
Prashant sangulgi wrote:
> Iam getting the mean from the image after that we have to find
> variance.. But to find variance what should i write in code.... Becz we
> have to substract all the values of the image with mean value... Then we
> get variance

>> lookfor variance
LSCOV Least squares with known covariance.
COV Covariance matrix.
VAR Variance.
ARCOV AR parameter estimation via covariance method.
ARMCOV AR parameter estimation via modified covariance method.
IMPINVAR Impulse invariance method for analog to digital filter conversion.
PCOV Power Spectral Density (PSD) estimate via the Covariance method.
PMCOV Power Spectral Density (PSD) estimate via the Modified Covariance
XCOV Cross-covariance function estimates.
SVCOV spectview Wrapper for Covariance method.
SVMCOV spectview Wrapper for Modified Covariance method.
>>

--
From: ImageAnalyst on 11 Aug 2010 10:21
On Aug 11, 7:53 am, "Prashant sangulgi" <psangu...(a)gmail.com> wrote:
> Iam getting the mean from the image after that we have to find  variance.. But to find variance what should i write in code.... Becz we have to substract all the values of the image with mean value... Then we get variance

--------------------------------------------------
There's this nifty feature of MATLAB called "help." If you bring up
the help facility, and type in variance and hit return, you'll see
that it brings up the var() function as the first topic. Click on
that and you should be on your way. It's done for you - you don't
need to get the mean of the squared difference of the image and the
mean like you said, although you can do it that way if you want.
Otherwise just follow the example in the help to get something like
varianceOfImage = var(monochromeImageArray(:));

Or for a more complete demo:
% Demo macro to extract frames and get frame means from an avi movie
% and save individual frames to separate image files.
clc;
close all;
workspace;
fontSize = 14;

% Change the current folder to the folder of this m-file.
% (The line of code below is from Brett Shoelson of The Mathworks.)
if(~isdeployed)
cd(fileparts(which(mfilename)));
end

% Read in standard MATLAB gray scale demo image.
grayImage = imread('cell.tif');
subplot(2, 1, 1);
imshow(grayImage, []);
title('Original Grayscale Image', 'FontSize', fontSize);
set(gcf, 'Position', get(0,'Screensize')); % Enlarge figure to full
screen.

% Just for fun, let's get its histogram.
[pixelCount grayLevels] = imhist(grayImage, 256);
subplot(2, 1, 2);
bar(pixelCount);
title('Histogram of original image', 'FontSize', fontSize);
xlim([0 grayLevels(end)]); % Scale x axis manually.
set(gca,'XTick',[0:10:grayLevels(end)]);

% Convert to double so you don't have clipping to the range 0-255.
doubleGrayImage = double(grayImage);
% Compute mean by two different methods:
meanBin = sum(pixelCount .* grayLevels) / sum(pixelCount) % Assumes
each bin is one gray level wide.
meanGrayLevel = mean2(doubleGrayImage)

% Compute variance by three different methods:
varianceOfImage1 = var(doubleGrayImage(:))
varianceOfImage2 = mean((doubleGrayImage(:) - meanGrayLevel).^2)
squareImage = doubleGrayImage .^2;
varianceOfImage3 = mean(squareImage(:)) - meanGrayLevel.^2
% Do it using histogram.
varianceOfImage4 = sum(pixelCount .* (grayLevels - meanBin).^2) /
sum(pixelCount)
varianceOfImage5 = sum(pixelCount .* grayLevels .^2) / sum(pixelCount)
- meanBin^2

% Compute standard deviation by four different methods:
StDevOfImage1 = std(doubleGrayImage(:))
StDevOfImage2 = sqrt(varianceOfImage1)
% Do it using histogram.
StDevOfImage3 = sqrt(sum(pixelCount .* (grayLevels - meanBin).^2) /
sum(pixelCount))
StDevOfImage4 = sqrt(sum(pixelCount .* grayLevels .^2) /
sum(pixelCount) - meanBin^2)

uiwait(msgbox('Done. Look in the command window.'));

 |  Next  |  Last
Pages: 1 2
Prev: use COM type library (.tlb) in matlab?
Next: GET PAID FOR WORKING ONLINE $100 ON YOUR FIRST DAY