homography1d.m

hopfield neural network for binary image recognition

% HOMOGRAPHY1D - computes 1D homography
%
% Usage:   H = homography1d(x1, x2)
%
% Arguments:
%          x1  - 2xN set of homogeneous points
%          x2  - 2xN set of homogeneous points such that x1<->x2
% Returns:
%          H - the 2x2 homography such that x2 = H*x1
%
% This code is modelled after the normalised direct linear transformation
% algorithm for the 2D homography given by Hartley and Zisserman p92.
%

% Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% pk @ csse uwa edu au
% http://www.csse.uwa.edu.au/~pk
%
% May 2003

function H = homography1d(x1, x2)

  % check matrix sizes
  if ~all(size(x1) == size(x2))
    error('x1 and x2 must have same dimensions');
  end
  
  % Attempt to normalise each set of points so that the origin 
  % is at centroid and mean distance from origin is 1.
  [x1, T1] = normalise1dpts(x1);
  [x2, T2] = normalise1dpts(x2);

  % Note that it may have not been possible to normalise
  % the points if one was at infinity so the following does not
  % assume that scale parameter w = 1.

  Npts = length(x1);
  A = zeros(2*Npts,4);

  for n = 1:Npts
    X = x1(:,n)';
    x = x2(1,n);  w = x2(2,n);
    A(n,:) = [-w*X x*X];
  end

  [U,D,V] = svd(A);

  % Extract homography
  H = reshape(V(:,4),2,2)';

  % Denormalise
  H = T2\H*T1;

  % Report error in fitting homography...

% NORMALISE1DPTS - normalises 1D homogeneous points
%
% Function translates and normalises a set of 1D homogeneous points 
% so that their centroid is at the origin and their mean distance from 
% the origin is 1.  
%
% Usage:   [newpts, T] = normalise1dpts(pts)
%
% Argument:
%   pts -  2xN array of 2D homogeneous coordinates
%
% Returns:
%   newpts -  2xN array of transformed 1D homogeneous coordinates
%   T      -  The 2x2 transformation matrix, newpts = T*pts
%           
% Note that if one of the points is at infinity no normalisation
% is possible.  In this case a warning is printed and pts is
% returned as newpts and T is the identity matrix.

function [newpts, T] = normalise1dpts(pts)

     if ~all(pts(2,:))
       warning('Attempt to normalise a point at infinity')
       newpts = pts;
       T = eye(2);
       return;
     end

     % Ensure homogeneous coords have scale of 1
     pts(1,:) = pts(1,:)./pts(2,:);

     c = mean(pts(1,:)')';      % Centroid.
     newp = pts(1,:)-c;         % Shift origin to centroid.

     meandist = mean(abs(newp));
     scale = 1/meandist;

     T = [scale    -scale*c
            0         1      ];

     newpts = T*pts;