Tugas Pengolahan Citra dan Pengenalan Pola 2

    1.  Construct a matrix f that is similar to the function f(m,n) in the example in “Definition of Fourier Transform” on page 9-3. Remember that f(m,n) is equal to 1 within the rectangular region and 0 elsewhere. Use a binary image to represent f(m,n).

f = zeros(30,30);

f(5:24,13:17) = 1;

imshow(f,'InitialMagnification','fit')

2. Compute and visualize the 30-by-30 DFT of f with these commands. Fourier Transform

F = fft2(f);

F2 = log(abs(F));

imshow(F2,[-1 5],'InitialMagnification','fit'); colormap(jet); colorbar

3. To obtain a finer sampling of the Fourier transform, add zero padding to f when computing its DFT. The zero padding and DFT computation can beperformed in a single step with this command.

                                           

4 The zero-frequency coefficient, however, is still displayed in the upper left corner rather than the center. You can fix this problem by using the function fftshift, which swaps the quadrants of F so that the zero-frequency coefficient is in the center.

bw = imread('text.png');

a = bw(32:45,88:98);

imshow(bw);

figure, imshow(a);

C = real(ifft2(fft2(bw) .* fft2(rot90(a,2),256,256)));

figure, imshow(C,[])

>> I = imread('cameraman.tif');

I = im2double(I);

T = dctmtx(8);

dct = @(x)T * x * T';

B = blkproc(I,[8 8],dct);

mask = [1 1 1 1 0 0 0 0

1 1 1 0 0 0 0 0

1 1 0 0 0 0 0 0

1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0];

B2 = blkproc(B,[8 8],@(x)mask.* x);

invdct = @(x)T' * x * T;

I2 = blkproc(B2,[8 8],invdct);

imshow(I), figure, imshow(I2)

Download disini