1. Images and MATLAB

2. Matlab Advantages
Every variable in Matlab is a multidimensional matrix.
Highly modular.
No memory allocation is necessary.
Matlab enables its own garbage collection.
Simple interface for complex mathematical concepts.
Even 1D variables.
In terms of matrix dimensions, and in terms of new functions
Memory issues should be regarded when dealing with large data sets.
Highly intuitive – most functions work for all dimension objects in the same manner.

3. Image Types
Intensity images scaled to represent intensities (uint8 – [0,255], double [0,1])
Binary images logical array of 0s and 1s
Indexed images Look up table [x, map]
RGB images truecolor, array of (m*n*3)
In case of logical arrays if the image is converted to logical any non zero element is converted to 1.
When working in intensity image it is important to convert to double when performing numerical operations like /
2 first types – grayscale
2 last types – color images
Map (3*number of colors), efficient representation
Checking the image type : isind, isbw, isgray, isrgb Converting image types: rgb2ind, rgb2gray, gray2ind, ind2gray,….

4. Data Classes
Numeric computations (8 first types) are done using double precision
Uint8 – when reading data from storage device (8 bit images)
Logical – output of logical operations (for example – mask threshing)
Conversion – same as casting
Converting between types : B = data_class_name(A) for example: B = double(A)

5. Conversions
When converting between data classes and types it is important to keep the value range for each data class
>> img = double(img)/255;
>> img = im2double(img);
Mat2gray – convert to full grayscale range [0 to 1]
Im2double - for other data classes
Im2bw – produce a binary image according to a threshold

6. MATLAB supported image formats
JPEG: Joint Photographics Experts Group
TIFF: Tagged Image File Format
GIF: Graphics Interchange Format
BMP: Microsoft Bitmap Format
PNG: Portable Network Graphics …

7. Displaying an image(cont.)
Spatial domain

8. Matlab Basics
Digital image representation : 2D function f(x,y) -> finite discrete quantities
Coordinate Conventions
img(r,c) r – rows (height) c – cols (width)
The first pixel: img(1,1)
Image is a 2D function in each location there is value representing the gray level of the image. Color image are formed by combination of 2D images. For example in the RGB color space each pixel has Red Green and Blue values.
Image is a discrete both in space (sampling) and in gray level values (quantization)
Matlab starts from (1,1) in comparison to other computer languages

9. Matlab 內建影像 C:\MATLAB7\toolbox\images\imdemos 皆為Matlab Help中範例的原始影像。
使用時只需直接在指令中輸入檔名，即可使用。
適用於觀察影像處理結果

10. Different Image Types Indexed images Intensity (grayscale) images
Binary images
RGB (true-color) images

11. Reading an image imread() 功用:將影像載入並存成array格式備用
用法:[I,map] = imread(filename);
I = imread(filename);
ex: I = imread('pout.tif');
I為指向影像的變數
不指定變數，則為ans

12. Displaying an image imshow() 功用:開啟一個視窗顯示影像 用法: imshow(I) imshow(I,map)
Figure, imshow()
功用:開啟一個新視窗顯示影像
用法: figure,imshow(I)

13. Displaying an image(cont.)
imshow(I, [low, high])
imshow(I, [ ])
功用:displays I as a grayscale intensity image, specifying the data range for I. The minimum value in I is displayed as black, and the maximum value is displayed as white.
pixval :
功能:cursor on image to show pixel values
用法: imshow(I),pixval

14. Displaying an image(cont.)
colorbar
功能:To display an image with a colorbar that indicates the range of intensity values.
用法： imshow(I), colorbar
　　ex:
I = imread('pout.tif');
imshow(I) , colorbar

15. Writing an image imwrite() 功能:將影像寫入成檔案 用法： imwrite(I,filename,format)
ex:
imwrite(I,'pout.jpg','JPEG');

16. Image information Image size: size() ex: I= imread('saturn.png');
size(I)
[M,N] = size(I)
M=影像I的高
N=影像I的寬

17. Image information whos 功用:display information about an image .
ex: whos I
Imfinfo( filename )
功用: display information about image file .
ex: info = imfinfo('saturn.png')

18. Digital Image processing
影像二元化
g = im2bw(I, T);
功用:Convert intensity image I to binary image g using threshold T, where T must be in range [0, 1].
ex:
I= imread('pout.tif');
g = im2bw(I, 0.4);
imshow(g); colorbar

19. Digital Image processing(cont.)
彩色轉灰階
Rgb2gray()
功用:將RBG彩色影像轉換成gray-level影像。ex:
I2= imread ('onion.png');
figure,imshow(I2); colorbar
g2 = rgb2gray(I2);
figure,imshow(g2); colorbar

20. Digital Image processing(cont.)反相
imcomplement( )
功用:The negative of an image.
ex:
I2= imread ('onion.png');
figure,imshow(I2); colorbar
J2 = imcomplement(g2);
figure, imshow(J2); colorbar

21. Grayscale images 灰階影像 Matlab example: imshow: display matrix
w=imread('pout.tif');
figure, imshow(w), pixval on
figure: create a window to place graphic object
imshow: display matrix
Data type of w?
291x240 uint8 (unsigned integer 8 bits

22. Binary image 二元影像 Matlab example: Data type of w?
w2=imread('circles.png');
figure, imshow(w2), pixval on
Data type of w?
256x256 logical
Pixel value is 0 or 1

23. RGB (true color) images 全彩影像
Matlab example:
w3=imread('peppers.png');
figure, imshow(w3), pixval on
size(w3)
w3(100,200,2)
w3(100,200,1:3)
w3(100,200,:)

24. RGB color model

25. Pixel depth
Pixel depth: the number of bits used to represent each pixel in RGB space
Full-color image: 24-bit RGB color image
(R, G, B) = (8 bits, 8 bits, 8 bits)

26. Indexed color image 彩色索引影像
Matlab example:
wI=imread('trees.tif');
figure, imshow(w), pixval on
What’s wrong?

27. Indices
Color Map

28. Indexed color image Matlab example:
[wI,wmap]=imread('trees.tif');
figure, imshow(wI, wmap)
How do we know it’s an indexed image?

29. Indexed color image 彩色索引影像
Matlab example:
w=imread(‘emu.tif’);
figure, imshow(w), pixval on
What’s wrong?

30. Indexed color image 6 10 15 12 5 11 20 10 … 4 6 10 7 indices Color map
Color map …
indices

31. Indexed color image Matlab example:
[w,wmap]=imread(‘emu.tif’);
imshow(w, wmap)
How do we know it’s an indexed image?

32. Get information about image
imfinfo('emu.tif');
Filename: 'emu.tif'
FileModDate: '12-Jul :40:00'
FileSize:
Format: 'tif'
FormatVersion: []
Width: 331
Height: 384
BitDepth: 8
ColorType: 'indexed'
ByteOrder: 'little-endian'
NewSubfileType: 0
BitsPerSample: 8
Colormap: [256x3 double]

33. Get information about image
imfinfo(‘emu.tif’);
Filename: 'emu.tif'
FileModDate: '12-Jul :40:00'
FileSize:
Format: 'tif'
FormatVersion: []
Width: 331
Height: 384
BitDepth: 8
ColorType: 'indexed'
ByteOrder: 'little-endian'
NewSubfileType: 0
BitsPerSample: 8
Colormap: [256x3 double]

34. Data types and conversion
uint8 image must be converted to double before any arithmetic operation
w=imread('pout.tif');
w=w+1; % fail
w=double(w); % data type is also conversion func.
w=w+1; % ok

35. Write image matrix to file
Matlab code
w=imread('pout.tif');
imwrite(w, 'pout.jpg','jpg');
General form
imwrite(X, map, ‘filename’, ‘format’);

36. Zooming and Shrinking Digital Images
Create a new pixel location.
Assign a gray-level to those new locations
Nearest neighbor interpolation
Pixel replication: a checkboard effect
Bilinear interpolation using four nearest neighbors
v(x’, y’)=ax’+by’+cx’y’+d
where a, b, c, and d are determined from the gray-level of the four neighbors.
Shrinking:
Direct shrinking causes aliasing
Expansion then Shrinking: blurring the image before shrinking it and reduce aliasing.

37. Sampling Methods of Inverse Mapping
If transformed pixel ‘X’ locates among
P1, P2, P3 and P4
Nearest neighbor method:
I(‘X’) = I(P3 )
where I(p) is the intensity value of pixel p
Bi-linear interpolation:
I(‘X’) = (1-a)(1-b)I( P1 ) + a(1-b)I(P2 )
+ (1-a)bI(P3 ) + abI(P4 )
where a, b are the fractional parts of ‘X’
Bi-cubic interpolation:
based on cubic splines P1 P2 P3 P4

38. Nearest neighbor interpolation
The closest neighbor is chosen , by rounding the “new” indexes to original image’s coordinates .

47. Digital Image processing(cont.)
變更影像大小
imresize(I,scale,method);
功用:To change the size of an image.
interpolation Method:
-'nearest‘ :Nearest-neighbor interpolation
-'bilinear‘ :Bilinear (the default)
-'bicubic‘ :Bicubic interpolation

48. Digital Image processing(cont.)
變更影像大小
imresize(I,scale,method);
功用:To change the size of an image.
interpolation Method:
-'nearest‘ :Nearest-neighbor interpolation
-'bilinear‘ :Bilinear (the default)
-'bicubic‘ :Bicubic interpolation

49. Digital Image processing(cont.)
ex:
I3 = imread('circuit.tif');
J3 = imresize(I3,1.25);
figure, imshow(I3)
figure, imshow(J3)
J4= imresize(I3,[ ], 'bilinear');
figure, imshow(J4)

50. Digital Image processing(cont.)
旋轉影像
imrotate(I, angle);
功用:To rotate an image.
ex:
I = imread('pout.tif');
J5 = imrotate(I,35);
figure, imshow(J5)

51. Contents Histogram Histogram transformation Histogram equalization
Contrast streching
Applications

55. Histogram
The (intensity or brightness) histogram shows how many
times a particular grey level (intensity) appears in an image.
For example, 0 - black, 255 – white 1 2 4 5
histogram
image

56. Histogram equalization (HE)
                               
Histogram equalization (HE)
transforms the intensity values so that the histogram of the output image approximately
matches the flat (uniform) histogram

63. Histogram equalization II.
                               
Histogram equalization II.
As for the discrete case the following formula applies:
k = 0,1,2,...,L-1
L: number of grey levels in image (e.g., 255)
nj: number of times j-th grey level appears in image
n: total number of pixels in the image
·(L-1) ?

66. Histogram equalization III
                               

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

69. Histogram equalization
ph=histeq(p);
imshow(ph), figure, imhist(ph), axis tight

70. Histogram equalization (cont.)
[ph, t]=histeq(p);
plot(t), title('transform function');
Exercise#1.
Apply histogram equalization
to tire image