this is an implementation for "how to use maximum likelihood in segmentation in image processing "
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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
2 Commenti
Image Analyst
il 1 Ago 2012
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
il 1 Ago 2012
Please review the guide to tags and retag this; see http://www.mathworks.co.uk/matlabcentral/answers/43073-a-guide-to-tags
Risposte (0)
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