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📁 hopfield neural network for binary image recognition
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<html><head><title>Example of edge detection, edge linking, and line segment fitting</title></head><body bgcolor="#ffffff" vlink="#ff0000"><h2>Example of edge detection, edge linking, and line segment fitting</h2><hr><center><img src=shapessm.jpg></center><pre>    % Read the sample image in    im = imread('shapessm.jpg');        % Find edges using the Canny operator with hysteresis thresholds of 0.1    % and 0.2 with smoothing parameter sigma set to 1.    edgeim = edge(im,'canny', [0.1 0.2], 1);    figure(1), imshow(edgeim);</pre><p><center><img src=edgeim.jpg></center><pre>        % Link edge pixels together into lists of sequential edge points, one    % list for each edge contour. A contour/edgelist starts/stops at an     % ending or a junction with another contour/edgelist.    % Here we discard contours less than 10 pixels long.    [edgelist, labelededgeim] = edgelink(edgeim, 10);        % Display the edgelists with random colours for each distinct edge     % in figure 2    drawedgelist(edgelist, size(im), 1, 'rand', 2); axis off        </pre><center><img src=edgelistim.jpg></center><pre>            % Fit line segments to the edgelists    tol = 2;         % Line segments are fitted with maximum deviation from		     % original edge of 2 pixels.    seglist = lineseg(edgelist, tol);    % Draw the fitted line segments stored in seglist in figure window 3 with    % a linewidth of 2 and random colours    drawedgelist(seglist, size(im), 2, 'rand', 3); axis off</pre><center><img src=segmentim.jpg></center><hr></body></html>

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