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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 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.  Discard contours less than 10 pixels long.    [edgelist, labelededgeim] = edgelink(edgeim, 10);        % Display the labeled edge image with separate colours for each    % distinct edge (choose your favorite colourmap!)    figure(2), imagesc(labelededgeim); colormap(vga), axis image, axis off</pre><center><img src=labelededgeim.jpg></center><pre>            % Fit line segments to the edgelists with the following parameters:    tol = 2;         % Line segments are fitted with maximum deviation from		     % original edge of 2 pixels.    angtol = 0.05;   % Segments differing in angle by less than 0.05 radians    linkrad = 2;     % and end points within 2 pixels will be merged.    [seglist, nedgelist] = lineseg(edgelist, tol, angtol, linkrad);    % Draw the fitted line segments stored in seglist in figure window 3 with    % a linewidth of 2    drawseg(seglist, 3, 2); axis off</pre><center><img src=segmentim.jpg></center><hr></body></html>

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