gaussian.m

正则化切割图像

function p=gaussian(x,m,C);
% p=gaussian(x,m,C);
%
% Evaluate the multi-variate density with mean vector m and covariance
% matrix C for the input vector x.
% 
% p=gaussian(X,m,C);
% 
% Vectorized version: Here X is a matrix of column vectors, and p is 
% a vector of probabilities for each vector.
% Jianbo Shi, 1997
d=length(m);

if size(x,1)~=d
   x=x';
end
N=size(x,2);

detC = det(C);
if rcond(C)<eps
%   fprintf(1,'Covariance matrix close to singular. (gaussian.m)\n');
   p = zeros(N,1);
else
   m=m(:);
   M=m*ones(1,N);
   denom=(2*pi)^(d/2)*sqrt(abs(detC));
   mahal=sum(((x-M)'*inv(C)).*(x-M)',2);   % Chris Bregler's trick
   numer=exp(-0.5*mahal);
   p=numer/denom;
end