Histogram 直方圖 Statistics of the pixel gray-levels of an ima

ge h(rk)=nk : histogram

gray level

no. of occurrence

histogram p=imread(‘pout.tif’); imshow(p), figure, imhist(p), axis tight

Histogram equalization

ph=histeq(p);

Histogram equalization (cont.)

[ph, t]=histeq(p); plot(t), title('transform function');

Exercise#1.Apply histogram equalizationto tire image

Filtering in MATLAB x=uint8(10*magic(5)); a=ones(3,3)/9; filter2(a,x,’same’); % output has same size, zero

padding

filter2(a,x,’valid’); % apply to inside of image

filter2(a,x,’full’); % apply to each intersection between mask

and image

ans =

76.6667 85.5556 65.5556 67.7778 58.8889 87.7778 111.1111 108.8889 128.8889 105.5556 66.6667 110.0000 130.0000 150.0000 106.6667 67.7778 131.1111 151.1111 148.8889 85.5556 56.6667 105.5556 107.7778 87.7778 38.8889

Exercise#2: average filtering

Use the test pattern to generate the following results.

original

5x5

15x15

3x3

9x9

35x35

Definition of 1st derivative in mask filters

Sobel operators

Exercise#3: 1st derivative Apply the Sobel filters to the lens image

水平邊

垂直邊 | 垂直邊 |+| 水平邊 |

Definition of 2nd derivatives in filter mask: Laplacian

900 rotationinvariant

450 rotationinvariant(include Diagonals)

4 -

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8

Exercise#4: sharpening filter

Generate the right images

original

Laplacianscaled sharpened