[regParams,Bfit,ErrorStats]=absorient(A,B,doScale)

function [regParams,Bfit,ErrorStats]=absorient(A,B,doScale)
% This tool solves the absolute orientation problem, i.e., it finds the 
% rotation, translation, and optionally also the scaling, that best maps one
% collection of 2D point coordinates to another in a least squares sense. 
% Namely,
%  
%            [regParams,Bfit,ErrorStats]=absorientParams(A,B,doScale)
% 
% solves, when doScale=false (the default),
%  
%                min. sum_i ||R*A(:,i) + t - B(:,i)||^2
%  
% where R is a 2D rotation matrix and t is a translation vector.
%  
%
%When doScale=true, it solves the more general problem
% 
%                min. sum_i ||s*R*A(:,i) + t - B(:,i)||^2
% 
%where s is a global scale factor. The registration uses Horn's
%quaternion-based algorithm.
%
%
%in:
%
%  A: a 2xN matrix whos columns are the 2D coords of N source points.
%  B: a 2xN matrix whos columns are the 2D coords of N target points.
%  doScale: Boolean flag. If true (default=false), the registration will 
%          include a scale factor.
%          
%out:
%
%
% regParams: structure output with estimated registration parameters,
%
%     regParams.R:   The estimated rotation
%     regParams.t:   The estimated translation
%     regParams.s:   The estimated scale factor (set to 1 if doScale=false).
%     regParams.M:   3x3 homogenous coordinate transform matrix [s*R,t;[0 0 1]].
%     regParams.q:   A unit quaternion [q0 qz] corresponding to R and
%                    signed so that R is a counter-clockwise rotation about
%                    the 3rd axis.
% 
%
%  Bfit: The rotation, translation, and scaling (as applicable) of A that 
%        best matches B in least squares sense.
%
%
% ErrorStats: structure output with error statistics. In particular,
%             defining err(i)=norm( Bfit(:,i)-B(:,i) ), it contains
%
%      ErrorStats.errlsq = 0.5* norm(err) 
%      ErrorStats.errmax = max(err) 
%
%
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Author: Matt Jacobson
% Copyright, Xoran Technologies, Inc.  http://www.xorantech.com



ncols=@(M) size(M,2); %number of columns
%matmvec=@(M,v) bsxfun(@minus,M,v); %matrix-minus-vector


nn=ncols(A);

if nargin<3, doScale=false; end

if nn~=ncols(B),
    error 'The number of points to be registered must be the same'
end



lc=mean(A,2);  rc=mean(B,2);  %Centroids

left  = matmvec(A,lc); %Center coordinates at centroids
right = matmvec(B,rc); 

M=left*right.';


Nxx=M(1)+M(4); Nyx=M(3)-M(2);

N=[Nxx   Nyx;...
   Nyx   -Nxx];

[V,D]=eig(N);

[trash,emax]=max(real(  diag(D)  )); emax=emax(1);

q=V(:,emax); %Gets eigenvector corresponding to maximum eigenvalue
q=real(q);   %Get rid of imaginary part caused by numerical error


q=q*sign(q(2)+(q(2)>=0)); %Sign ambiguity
q=q./norm(q);

R11=q(1)^2-q(2)^2;
R21=prod(q)*2;

R=[R11 -R21;R21 R11]; %map to orthogonal matrix


if doScale
   
     summ = @(M) sum(M(:));
  
     sss=summ( right.*(R*left))/summ(left.^2);
     t=rc-R*(lc*sss);
     
     
else
    
    sss=1;
    t=rc-R*lc;
   
 
end



regParams.R=R;
regParams.t=t;
regParams.s=sss;
regParams.M=[sss*R,t;[0 0 1]];
regParams.q=q;

if nargout>1
    
    Bfit=matmvec((sss*R)*A,-t);
    
end

if nargout>2
    
    l2norm = @(M,dim) sqrt(sum(M.^2,dim));

    err=l2norm(Bfit-B,1);
    
    ErrorStats.errlsq=0.5*norm(err);
    ErrorStats.errmax=max(err);
    
   
end
    

function M=matmvec(M,v)
%Matrix-minus-vector

 M(1,:)=M(1,:)-v(1);
 M(2,:)=M(2,:)-v(2);