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61 lines
1.4 KiB
61 lines
1.4 KiB
clear
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clear all
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% For reproducibility
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rng(1);
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load('dip_hw_2.mat');
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%% Produces the affinity graphs for both images
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graph1 = Image2Graph(d2a);
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graph2 = Image2Graph(d2b);
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%% Executes non recursive experiments for the first image
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figure();
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imshow(d2a);
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clusters = myNCuts(graph1, 2);
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clusters = clusters ./ 2;
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clusters = reshape(clusters, size(d2a, 1), []);
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figure();
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imshow(clusters);
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clusters = myNCuts(graph1, 3);
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clusters = clusters ./ 3;
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clusters = reshape(clusters, size(d2a, 1), []);
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figure();
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imshow(clusters);
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clusters = myNCuts(graph1, 4);
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clusters = clusters ./ 4;
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clusters = reshape(clusters, size(d2a, 1), []);
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figure();
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imshow(clusters);
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%% Executes non recursive experiments for the second image
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figure();
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imshow(d2b);
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clusters = myNCuts(graph2, 2);
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figure();
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imshow(meanClustersColorRGB(d2b, reshape(clusters, size(d2b, 1), [])));
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% clusters = clusters ./ 2;
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% clusters = reshape(clusters, size(d2a, 1), []);
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% figure();
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% imshow(clusters);
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clusters = myNCuts(graph2, 3);
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figure();
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imshow(meanClustersColorRGB(d2b, reshape(clusters, size(d2b, 1), [])));
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% clusters = clusters ./ 3;
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% clusters = reshape(clusters, size(d2a, 1), []);
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% figure();
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% imshow(clusters);
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clusters = myNCuts(graph2, 4);
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figure();
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imshow(meanClustersColorRGB(d2b, reshape(clusters, size(d2b, 1), [])));
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% clusters = clusters ./ 4;
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% clusters = reshape(clusters, size(d2a, 1), []);
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% figure();
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% imshow(clusters);
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