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

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

[Image Analyst]

If you think this would be generally useful to lots of other people, then the File Exchange would be the more appropriate place to post this.

[Walter Roberson]

Please review the guide to tags and retag this; see http://www.mathworks.co.uk/matlabcentral/answers/43073-a-guide-to-tags