Пример 12.2. Байесовская классификация мультиспектральных данных.

f1 = imread('C: \Users\Alex\Desktop\Washington1.tif');

f2 = imread('C: \Users\Alex\Desktop\Washington2.tif');

f3 = imread('C: \Users\Alex\Desktop\Washington3.tif');

f4 = imread('C: \Users\Alex\Desktop\Washington4.tif');

B1 = roipoly(f1);

B2 = roipoly(f2);

B3 = roipoly(f3);

stack = cat(3, f1, f2, f3, f4);

[Y1, R1] = imstack2vectors(stack, B1);

[Y2, R2] = imstack2vectors(stack, B2);

[Y3, R3] = imstack2vectors(stack, B3);

[C1, m1] = covmatrix(Y1);

[C2, m2] = covmatrix(Y2);

[C3, m3] = covmatrix(Y3);

CA = cat(3, C1, C2, C3);

MA = cat(2, m1, m2, m3);

dY1 = bayesgauss(Y1, CA, MA);

IY1 = find(dY1 ~= 1);

Пример 12.3. Распознавание объектов на основе сопоставления строк.

B = imread('C: \Users\Alex\Desktop\bottle1.tif');

b = boundaries (B, 4, 'cw');

b = b{1};

[M, N] = size(B);

xmin = min(b(:, 1));

ymin = min(b(:, 2));

[x, y] = minperpoly(B, 8);

b2 = connectpoly(x, y);

B2 = bound2im(b2, M, N, xmin, ymin);

imshow(B2)

[xn, yn] = randvertex(x, y, 5);

b3 = connectpoly(xn, yn);

B3 = bound2im(b3, M, N, xmin, ymin);

imshow(B3)

angles = polyangles(x, y);

s = floor(angles/45) + 1;

s = int2str(s);

R = strsimilarity(s11, s12);