pixel based classification .
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I have to do pixel based classification. for that I have used texture feature of entire image (like wavelet,lows). now I have to get neighbourwood sliding window(3,5,7) of this image and replace central pixel with the avg of neighbour and calulate mean,varience, energy of this and make a feature vector where rows are sample space and column are feature space. So far I have got the wavelet trasform how to procced for windowing process?
my approch- calculate texture (wavelet trasform) take nxn(3,5,7) neighbourhood calculate energy,mean,skewness,kurtosis in nXn neighbourhood(dont know how to get?)
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doc nlfilter
should help for a 2d image. It contains an example for median. Mean, variance, etc will be similar.
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Image Analyst
2016-9-1
I'm going to assume you really didn't mean "replace the avg of neighbour with central pixel" and actually meant "replace the central pixel with the average of its neighbours."
For that, you can use imfilter() or conv2():
windowSize = 5;
kernel = one(windowSize)/ windowSize^2;
meanImage = conv2(grayImage, kernel, 'same');
meanImage = imfilter(grayImage, kernel); % Alternative
That will be much faster than nlfilter. For the others you'll still need nlfilter.
Image Analyst
2016-9-1
You need to give a dimension/direction. See the help. If you don't, they give the computations on a "per column" basis, which is why you ended up with a row vector.
To get the value for an entire image:
meanOfEntireImage = mean(grayImage(:)); % or
meanOfEntireImage = mean2(grayImage);
For a 2D array you can use mean2(), std2(), medfilt2().
ashu
2016-9-1
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