How code GMM RGB image segmentation in matlab?
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이전 댓글 표시
GMM using Covariance and not grayscale image 1 D... I want use RGB image using GMM
댓글 수: 2
Adam
2019년 11월 29일
If you have the statistics toolbox then
doc gmdistribution
should help. If not then you can search the File Exchange or program it yourself.
Image Analyst
2019년 12월 15일
Original question:
GMM using Covariance and not grayscale image 1 D... I want use RGB image using GMM
답변 (1개)
Fowzi barznji
2020년 3월 3일
Try this code
clc;
[file,path] = uigetfile('*.jpg');
disp(['User selected ', fullfile(path,file)]);
img=imread(fullfile(path,file));
EMSeg(img,3);
% you can change thne number of clusters (3) to another choice number
댓글 수: 2
Fowzi barznji
2020년 3월 3일
here the GMM Function u should use to call it
function [mask,mu,v,p]=EMSeg(ima,k)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Expectation Maximization image segmentation
%
% Input:
% ima: grey color image
% k: Number of classes
% Output:
% mask: clasification image mask
% mu: vector of class means
% v: vector of class variances
% p: vector of class proportions
%
% Example: [mask,mu,v,p]=EMSeg(image,3);
%
% Author: Prof. Jose Vicente Manjon Herrera
% Email: jmanjon@fis.upv.es
% Date: 02-05-2006
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% check image
ima=double(ima);
copy=ima; % make a copy
ima=ima(:); % vectorize ima
mi=min(ima); % deal with negative
ima=ima-mi+1; % and zero values
m=max(ima);
s=length(ima);
% create image histogram
h=histogram(ima);
x=find(h);
h=h(x);
x=x(:);h=h(:);
% initiate parameters
mu=(1:k)*m/(k+1);
v=ones(1,k)*m;
p=ones(1,k)*1/k;
% start process
sml = mean(diff(x))/1000;
while(1)
% Expectation
prb = distribution(mu,v,p,x);
scal = sum(prb,2)+eps;
loglik=sum(h.*log(scal));
%Maximizarion
for j=1:k
pp=h.*prb(:,j)./scal;
p(j) = sum(pp);
mu(j) = sum(x.*pp)/p(j);
vr = (x-mu(j));
v(j)=sum(vr.*vr.*pp)/p(j)+sml;
end
p = p + 1e-3;
p = p/sum(p);
% Exit condition
prb = distribution(mu,v,p,x);
scal = sum(prb,2)+eps;
nloglik=sum(h.*log(scal));
if((nloglik-loglik)<0.0001) break; end;
clf
plot(x,h);
hold on
plot(x,prb,'g--')
plot(x,sum(prb,2),'r')
drawnow
end
% calculate mask
mu=mu+mi-1; % recover real range
s=size(copy);
mask=zeros(s);
for i=1:s(1),
for j=1:s(2),
for n=1:k
c(n)=distribution(mu(n),v(n),p(n),copy(i,j));
end
a=find(c==max(c));
mask(i,j)=a(1);
end
end
function y=distribution(m,v,g,x)
x=x(:);
m=m(:);
v=v(:);
g=g(:);
for i=1:size(m,1)
d = x-m(i);
amp = g(i)/sqrt(2*pi*v(i));
y(:,i) = amp*exp(-0.5 * (d.*d)/v(i));
end
function[h]=histogram(datos)
datos=datos(:);
ind=find(isnan(datos)==1);
datos(ind)=0;
ind=find(isinf(datos)==1);
datos(ind)=0;
tam=length(datos);
m=ceil(max(datos))+1;
h=zeros(1,m);
for i=1:tam,
f=floor(datos(i));
if(f>0 & f<(m-1))
a2=datos(i)-f;
a1=1-a2;
h(f) =h(f) + a1;
h(f+1)=h(f+1)+ a2;
end;
end;
h=conv(h,[1,2,3,2,1]);
h=h(3:(length(h)-2));
h=h/sum(h);
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