% Demonstration of feature matching via simple correlation, and then using% RANSAC to estimate the fundamental matrix and at the same time identify% (mostly) inlying matches% Peter Kovesi  % School of Computer Science & Software Engineering% The University of Western Australia% pk at csse uwa edu au% http://www.csse.uwa.edu.au/~pk%% February 2004% August   2005  Octave versionfunction testfundOctave    close all            thresh = 500;   % Harris corner threshold    nonmaxrad = 3;  % Non-maximal suppression radius    dmax = 50;    w = 11;    % Window size for correlation matching        im1 = imread('im02.jpg');    im2 = imread('im03.jpg');    % Find Harris corners in image1 and image2    [cim1, r1, c1] = harris(im1, 1, thresh, 3);%    show(im1,1), hold on, plot(c1,r1,'r+');    [cim2, r2, c2] = harris(im2, 1, thresh, 3);%    show(im2,2), hold on, plot(c2,r2,'r+');    drawnow    [m1,m2] = matchbycorrelation(im1, [r1';c1'], im2, [r2';c2'], w, dmax);    % Display putative matches%    show(im1,3), set(3,'name','Putative matches'), hold on        for n = 1:length(m1);	line([m1(2,n) m2(2,n)], [m1(1,n) m2(1,n)])    end    % Assemble homogeneous feature coordinates for fitting of the    % fundamental matrix, note that [x,y] corresponds to [col, row]    x1 = [m1(2,:); m1(1,:); ones(1,length(m1))];    x2 = [m2(2,:); m2(1,:); ones(1,length(m1))];            t = .001;  % Distance threshold for deciding outliers    [F, inliers] = ransacfitfundmatrix(x1, x2, t);    fprintf('Number of inliers was %d (%d%%) \n', ...	    length(inliers),round(100*length(inliers)/length(m1)))    fprintf('Number of putative matches was %d \n', length(m1))                % Display both images overlayed with inlying matched feature points%    show(double(im1)+double(im2),4), set(4,'name','Inlying matches'), hold on     plot(m1(2,inliers),m1(1,inliers),'r+');    plot(m2(2,inliers),m2(1,inliers),'g+');        for n = inliers	line([m1(2,n) m2(2,n)], [m1(1,n) m2(1,n)],'color',[0 0 1])    end           % Step through each matched pair of points and display the    % corresponding epipolar lines on the two images.        l2 = F*x1;    % Epipolar lines in image2    l1 = F'*x2;   % Epipolar lines in image1        % Solve for epipoles    [U,D,V] = svd(F,0);    e1 = hnormalise(V(:,3));    e2 = hnormalise(U(:,3));        return        for n = inliers	figure(1), clf, imshow(im1), hold on, plot(x1(1,n),x1(2,n),'r+');	hline(l1(:,n)); plot(e1(1), e1(2), 'g*');	figure(2), clf, imshow(im2), hold on, plot(x2(1,n),x2(2,n),'r+');	hline(l2(:,n)); plot(e2(1), e2(2), 'g*');	fprintf('hit any key to see next point\r'); pause    end    fprintf('                                         \n');