this can be done by maximum likelihood classification of the images
and finding the corresponding classes.
can anybody help me in the code for this or any thing helpful.
i shall be thankful.
This is the main formula after calculating the mean value and the
covariance for each training area.
for i2=1:number_of_classes;
g{i2} = double((-log(det(covariance{i2}))-(x-m{i2})))*inv
(covariance{i2})*(x-m{i2})';
end
Hope this helps
Bill
but can u tell me that how u computed the covariance for the RGB
image i.e when ROI(region of intrest) is supplied to the algorithm
then how it calculates the covariance and then the likelihood for
each class.
i mean the code snippet for mean,covariance,and then the
corresponding likelihood for that ROI.
Try this it might solve your problem