this is an implementation for "how to use maximum likelihood in segmentation in image processing "

gehad attia

2012 8 1

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function ML()
image=zeros(256,256);
image(256/4:3*(256/4),256/4:3*(256/4))=200;
%object1=image(256/4:3*(256/4),256/4:3*(256/4));
image(10:60,10:60)=150;
%object2=image(10:60,10:60);
image=image/255;
imshow(image);
[r c]=size(image);
s_avg = sum(sum(image))/(r*c);
SNR=10;
n_sigma=s_avg/(10^(SNR/20));
n=n_sigma*randn(size(image));
image=image+n;
figure,hist(image);
figure,imshow(image);
%-----------PDF of the intensity of a background pixel---------
backgound=image(1:50,70:150);
backgound_pdf=normpdf(backgound,0,1);
%figure,plot(backgound,backgound_pdf);
%----------PDF of the intensity of an object pixel----------
object=image(256/4:3*(256/4),256/4:3*(256/4));
object_pdf=normpdf(object,200,1);
%figure,plot(object,object_pdf);
array=[0 0];
k=1;
   for i=1:size(image,1)
       for j=1:size(image,2)
           %if  p(y|black) < p(y|object) then x=object else x=BG
           if 1/(sqrt(2*pi)*n_sigma)*exp(-1*((image(i,j)-0)^2/(2*n_sigma^2)) ) <= 1/(sqrt(2*pi)*1)*exp(-1*((image(i,j)-0)^2/(2*1) ))
               array(k,:)=[i j];
               k=k+1;
          end
       end
   end
   map=[];
   plotting(array,image,map);
end
function plotting(FParray,fseg,map)
colormap(map)
imshow(fseg);
axis off
hold on
FPSize= size(FParray,1);
for i=1:FPSize
        rectangle('Position',[FParray(i,1), FParray(i,2), 1, 1],'Curvature',
  [1,1],'FaceColor','r','EdgeColor','r');
end
f=getframe(gca);
[X, map] = frame2im(f);
%imwrite(X,'FeaturePoints.png','png')
end