%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% MixelSigma
% ----------
% AUTHOR: Stephen Rose, Maher Khoury
% DATE: March 1, 1999
% PURPOSE:
% Calculate the
variance of the mixels from the expected
% values based
on one-quarter weighting from cardinal
% neighbors
%
% Notes:
% -once MixelDiff is found, the value of the variance
is found graphically
%
% Variables:
% -AvgMixel = just
to see the mean value of the mixels
% -miHat
= expected mixel value based on cardinal neighbors
% -MixelDiff = difference
between expected mxiel value and true mixel value
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
mixels = double(imread('mixelstempfinal.bmp'));
AvgMixel=mean(mean(mixels));
MixelDiff = zeros(size(mixels,1)-2,size(mixels,2)-2);
for k=2:(size(mixels,1)-1),
for l=2:(size(mixels,2)-1),
miHat=(0.25*mixels((k-1),l))+(0.25*mixels((k+1),l))+(0.25*mixels(k,(l-1)))+(0.25*
mixels(k,(l+1)));
MixelDiff(k-1,l-1)=mixels(k,l)-miHat;
end
end
%%%Look at data
hist(MixelDiff(:),[-15:0.5:15])
%%%Read off this value for test dollar bill test image
MixelSigma=2.75;