www.pudn.com > MI_Optimization.rar > image_registr_MI.m

function f=image_registr_MI(x) 
 
% This is a subroutine of opti_MI_scaling.m. 
% 
% Originally written by K.Artyushkova 
% 10_2003 
% Kateryna Artyushkova 
% Postdoctoral Scientist 
% Department of Chemical and Nuclear Engineering 
% The University of New Mexico 
% (505) 277-0750 
% kartyush@unm.edu  
 
% Modified by Hosang Jin 
% 2_2005 
%  
% Graduate Student 
% Department of Nuclear & Radiological Engineering 
% The University of Florida 
% hsjin@ufl.edu 
 
load image 
 
IM1=double(IM1); 
IM2=double(IM2); 
IM2=imresize(IM2, x(4), 'bilinear'); 
J=imrotate(double(IM2), x(3),'bilinear'); %rotated cropped IMAGE2 
J=abs(J)*255/max(max(J)); 
 
[n1 n2]=size(IM1); 
[n3 n4]=size(J); 
 
if n1>n3-x(1)/2 
    f=1000; 
    message=strvcat('The scaling factor is too small.', 'Press Ctrl+C to stop.',... 
        'Increase x0(4) and restart.'); 
    disp('Press Ctrl+C to stop.') 
    Errordlg(message) 
    pause; 
else 
    if x(1)>n3-n1 
        x(1)=n3-n1-1; 
        IM1(1:n1, 1:n2)=255; 
    end 
     
    if x(2)>n4-n2 
        x(2)=n4-n2-1; 
        IM1(1:n1, 1:n2)=255; 
    end 
     
    if x(1)<0 
        x(1)=0; 
        IM1(1:n1, 1:n2)=255; 
    end 
     
    if x(2)<0 
        x(2)=0; 
        IM1(1:n1, 1:n2)=255; 
    end 
     
    xt=1:n1; 
    yt=1:n2; 
     
    xx=round(xt+x(1)); 
    yy=round(yt+x(2)); 
     
    IM2=round(J(xx, yy)); % selecting part of IMAGE2 matching the size of IMAHE1 
     
    rows=size(IM1,1); 
    cols=size(IM2,2); 
    N=256; 
     
    h=zeros(N,N); 
     
    for ii=1:rows;    %  col  
        for jj=1:cols;   %   rows 
            h(IM1(ii,jj)+1,IM2(ii,jj)+1)= h(IM1(ii,jj)+1,IM2(ii,jj)+1)+1; 
        end 
    end 
     
    [r,c] = size(h); 
    b= h./(r*c); % normalized joint histogram 
    y_marg=sum(b); %sum of the rows of normalized joint histogram 
    x_marg=sum(b');%sum of columns of normalized joint histogran 
     
    Hy=0; 
    for i=1:c;    %  col 
        if( y_marg(i)==0 ) 
            %do nothing 
        else 
            Hy = Hy + -(y_marg(i)*(log2(y_marg(i)))); %marginal entropy for image 1 
        end 
    end 
     
    Hx=0; 
    for i=1:r;    %rows 
        if( x_marg(i)==0 ) 
            %do nothing 
        else 
            Hx = Hx + -(x_marg(i)*(log2(x_marg(i)))); %marginal entropy for image 2 
        end    
    end 
    h_xy = -sum(sum(b.*(log2(b+(b==0))))); % joint entropy 
     
    f=-(Hx+Hy-h_xy);% Mutual information 
    %x 
end