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DEM: Diffused expectation maximisation for image segmentation

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DEM: Diffused expectation maximisation for image segmentation

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Color image segmentation using a variant of the Expectation-Maximization (EM) algorithm.

normalise(A, dim)
function [M, z] = normalise(A, dim)
% NORMALISE Make the entries of a (multidimensional) array sum to 1
% [M, c] = normalise(A)
% c is the normalizing constant
%
% [M, c] = normalise(A, dim)
% If dim is specified, we normalise the specified dimension only,
% otherwise we normalise the whole array.

if nargin < 2
  z = sum(A(:));
  % Set any zeros to one before dividing
  % This is valid, since c=0 => all i. A(i)=0 => the answer should be 0/1=0
  s = z + (z==0);
  M = A / s;
elseif dim==1 % normalize each column
  z = sum(A);
  s = z + (z==0);
  %M = A ./ (d'*ones(1,size(A,1)))';
  M = A ./ repmatC(s, size(A,1), 1);
else
  % Keith Battocchi - v. slow because of repmat
  z=sum(A,dim);
  s = z + (z==0);
  L=size(A,dim);
  d=length(size(A));
  v=ones(d,1);
  v(dim)=L;
  %c=repmat(s,v);
  c=repmat(s,v');
  M=A./c;
end


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