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📁 hopfield neural network for binary image recognition
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 3.2//EN"><html><head><title>Example of finding the fundamental matrix using RANSAC</title></head><body bgcolor="#ffffff" vlink="#ff0000"><h2>Example of finding the fundamental matrix using RANSAC</h2><hr><center><table><tr><td><a href=im1.jpg><img src=im1.jpg></a><br> im1.jpg    <td><a href=im2.jpg><img src=im2.jpg></a><br> im2.jpg</table></center><pre>    thresh = 500;   % Harris corner threshold    nonmaxrad = 3;  % Non-maximal suppression radius        im1 = imread('im1.jpg');    im2 = imread('im2.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+');</pre><center><table><tr><td><img src=im1c.jpg>    <td><img src=im2c.jpg></table></center><pre>    w = 7;    % Window size for correlation matching    [m1,m2] = matchbycorrelation(im1, [r1';c1'], im2, [r2';c2'], w);    % 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</pre><center><img src=putative.jpg></center><pre>    % 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);    % 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</pre><center><img src=inliers.jpg></center><pre>    % 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    for n = inliers	figure(1), clf, show(im1,1), hold on, plot(x1(1,n),x1(2,n),'r+');	hline(l1(:,n));	figure(2), clf, show(im2,2), hold on, plot(x2(1,n),x2(2,n),'r+');	hline(l2(:,n));	fprintf('hit any key to see next point\r'); pause    end</pre><center><table><tr><td><img src=match1.jpg>    <td><img src=match2.jpg></table></center><a href=testfund.m>testfund.m</a> download the code above<hr></body></html>

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