% MATCHBYCORRELATION - match image feature points by correlation
%
% Function generates putative matches between previously detected
% feature points in two images by looking for points that are maximally
% correlated with each other within windows surrounding each point.
% Only points that correlate most strongly with each other in *both*
% directions are returned.
% This is a simple-minded N^2 comparison.
%
% Usage: [m1, m2, p1ind, p2ind, cormat] = ...
% matchbycorrelation(im1, p1, im2, p2, w, dmax)
%
% Arguments:
% im1, im2 - Images containing points that we wish to match.
% p1, p2 - Coordinates of feature pointed detected in im1 and
% im2 respectively using a corner detector (say Harris
% or phasecong2). p1 and p2 are [2xnpts] arrays though
% p1 and p2 are not expected to have the same number
% of points. The first row of p1 and p2 gives the row
% coordinate of each feature point, the second row
% gives the column of each point.
% w - Window size (in pixels) over which the correlation
% around each feature point is performed. This should
% be an odd number.
% dmax - (Optional) Maximum search radius for matching
% points. Used to improve speed when there is little
% disparity between images. Even setting it to a generous
% value of 1/4 of the image size gives a useful
% speedup. If this parameter is omitted it defaults to Inf.
%
%
% Returns:
% m1, m2 - Coordinates of points selected from p1 and p2
% respectively such that (putatively) m1(:,i) matches
% m2(:,i). m1 and m2 are [2xnpts] arrays defining the
% points in each of the images in the form [row;col].
% p1ind, p2ind - Indices of points in p1 and p2 that form a match. Thus,
% m1 = p1(:,p1ind) and m2 = p2(:,p2ind)
% cormat - Correlation matrix; rows correspond to points in p1,
% columns correspond to points in p2
% Copyright (c) 2004-2009 Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% http://www.csse.uwa.edu.au/
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% February 2004 - Original version
% May 2004 - Speed improvements + constraint on search radius for
% additional speed
% August 2004 - Vectorized distance calculation for more speed
% (thanks to Daniel Wedge)
% December 2009 - Added return of indices of matching points from original
% point arrays
function [m1, m2, p1ind, p2ind, cormat] = ...
matchbycorrelation(im1, p1, im2, p2, w, dmax)
if nargin == 5
dmax = Inf;
end
im1 = double(im1);
im2 = double(im2);
% Subtract image smoothed with an averaging filter of size wXw from
% each of the images. This compensates for brightness differences in
% each image. Doing it now allows faster correlation calculation.
im1 = im1 - filter2(fspecial('average',w),im1);
im2 = im2 - filter2(fspecial('average',w),im2);
% Generate correlation matrix
cormat = correlatiomatrix(im1, p1, im2, p2, w, dmax);
[corrows,corcols] = size(cormat);
% Find max along rows give strongest match in p2 for each p1
[mp2forp1, colp2forp1] = max(cormat,[],2);
% Find max down cols give strongest match in p1 for each p2
[mp1forp2, rowp1forp2] = max(cormat,[],1);
% Now find matches that were consistent in both directions
p1ind = zeros(1,length(p1)); % Arrays for storing matched indices
p2ind = zeros(1,length(p2));
indcount = 0;
for n = 1:corrows
if rowp1forp2(colp2forp1(n)) == n % consistent both ways
indcount = indcount + 1;
p1ind(indcount) = n;
p2ind(indcount) = colp2forp1(n);
end
end
% Trim arrays of indices of matched points
p1ind = p1ind(1:indcount);
p2ind = p2ind(1:indcount);
% Extract matched points from original arrays
m1 = p1(:,p1ind);
m2 = p2(:,p2ind);
%-------------------------------------------------------------------------
% Function that does the work. This function builds a correlation matrix
% that holds the correlation strength of every point relative to every
% other point. While this seems a bit wasteful we need all this data if
% we want to find pairs of points that correlate maximally in both
% directions.
%
% This code assumes im1 and im2 have zero mean. This speeds the
% calculation of the normalised correlation measure.
function cormat = correlatiomatrix(im1, p1, im2, p2, w, dmax)
if mod(w, 2) == 0
error('Window size should be odd');
end
[rows1, npts1] = size(p1);
[rows2, npts2] = size(p2);
% Initialize correlation matrix values to -infinty
cormat = -ones(npts1,npts2)*Inf;
if rows1 ~= 2 | rows2 ~= 2
error('Feature points must be specified in 2xN arrays');
end
[im1rows, im1cols] = size(im1);
[im2rows, im2cols] = size(im2);
r = (w-1)/2; % 'radius' of correlation window
% For every feature point in the first image extract a window of data
% and correlate with a window corresponding to every feature point in
% the other image. Any feature point less than distance 'r' from the
% boundary of an image is not considered.
% Find indices of points that are distance 'r' or greater from
% boundary on image1 and image2;
n1ind = find(p1(1,:)>r & p1(1,:)r & p1(2,:)r & p2(1,:)r & p2(2,:)