The document provides an overview of basic image processing concepts and techniques using MATLAB, including:
- Reading and displaying images
- Performing operations on image matrices like dilation, erosion, and thresholding
- Segmenting images using global and local thresholding methods
- Identifying and labeling connected components
- Extracting properties of connected components using regionprops
- Performing tasks like edge detection and noise removal
Code examples and explanations are provided for key functions like imread, imshow, imdilate, imerode, im2bw, regionprops, and edge.
4. Basics of Image Processing using MATLAB
Activity Recognition
Image Stitching
5. Basics of Image Processing using MATLAB
Medical Image Enhancement
Image Morphing
6. Basics of Image Processing using MATLAB
Basic operation with Matrices
% Use colon at the end to suppress % Transpose of a matrix
% output A_trans = A’
% To enter a matrix with real elements A_trans =
A = [5 3 7; 8 9 2; 1 4.2 6e-2]
5.0000 8.0000 1.0000
A = 3.0000 9.0000 4.2000
7.0000 2.0000 0.0600
5.0000 3.0000 7.0000
8.0000 9.0000 2.0000 % Matrix addition
1.0000 4.2000 0.0600 C = A + A_trans
% To enter a matrix with complex C =
% elements
X = [5+3*j 7+8j; 9+2j 1+4j;] 10.0000 11.0000 8.0000
11.0000 18.0000 6.2000
X = 8.0000 6.2000 0.1200
5.0000 + 3.0000i 7.0000 + 8.0000i
9.0000 + 2.0000i 1.0000 + 4.0000i
7. Basics of Image Processing using MATLAB
Basic operation with Matrices
% Matrix multiplication (element wise)
C = A .* A_trans
C =
25.0000 24.0000 7.0000
These commands or functions can be run in the
24.0000 81.0000 8.4000
MATLAB command prompt or as Script files (.m)
7.0000 8.4000 0.0036
Either
% Matrix multiplication • type edit <filename>.m in MATLAB command
C = A * A_trans
prompt to open the editor or
• go File - New - Blank M File
C =
83.0000 81.0000 18.0200
81.0000 149.0000 45.9200
18.0200 45.9200 18.6436
8. Basics of Image Processing using MATLAB
A Simple Character Recognition Code
• Detect only particular characters and
numbers in an image.
• Characters are in white and of a fixed
size.
• Background is black in color.
• The image is in binary format.
We will explore various concepts as we
implement this.
9. Basics of Image Processing using MATLAB
Reading images in MATLAB
% Set working directory to directory
% containing this tutorial
% Reading an image
% A = IMREAD(FILENAME,FMT) or
% A = IMREAD('FILENAME.FMT')
im=imread('.char recogtestimage.bmp');
% It is better to suppress outputs when
% reading images. Try once without the
% colon at the end of command
% Displaying an image
imshow(im)
% To open a separate window for the
% figure and not overwrite in the
% existing window
figure
imshow(im)
figure, imshow(im)
10. Basics of Image Processing using MATLAB
Reading images in MATLAB
Now read the image ‘same color.jpg’ and display it on a window.
Once the image is displayed in the window, select Tools – Data Cursor or select the shortcut on the
toolbar.
Click on point A as shown, on the image. It displays three values (RGB) since it is a color image. You
can try reading pixel values for the previous image. It will be either 0/1 since it is binary image.
Hold Alt and Click on point B. This creates something called as a new datatip.
Now for some fun
B
A
11. Basics of Image Processing using MATLAB
Reading images in MATLAB
Now read the image ‘same color.jpg’ and display it on a window.
Once the image is displayed in the window, select Tools – Data Cursor or select the shortcut on the
toolbar.
Click on point A as shown, on the image. It displays three values (RGB) since it is a color image. You
can try reading pixel values for the previous image. It will be either 0/1 since it is binary image.
Hold Alt and Click on point B. This creates something called as a new datatip.
Now for some fun
What are the RGB values at the two points? B
A
Adelson's checker shadow illusion (http://en.wikipedia.org/wiki/Same_color_illusion)
12. Basics of Image Processing using MATLAB
Writing functions in MATLAB
Let’s write a function charrec(im)which when called with an image file, will
display the characters as shown earlier
>> im=imread('.char recogtestimage.bmp');
>> imshow(im);
>> charrec(im);
The digits found in the image are:
0
3
5
The letters found in the image are:
L
N
13. Basics of Image Processing using MATLAB
Writing functions in MATLAB
Few examples of functions in MATLAB
% Function returning no output
function sample(ip1,ip2,ip3,…)
.
.
.
end
% Function with outputs
function [op1,op2,…]=sample(ip1,ip2,ip3,…)
.
.
.
End
% save the code as sample.m. Function name
% and m-file name should be the same
14. Basics of Image Processing using MATLAB
The Algorithm
Dilation
• adds pixels to the boundaries of objects in an image.
• number of pixels added from the objects in an image
depends on the size and shape of the structuring element
• function strel(…) can be used to generate the SEs.
>> SE = strel('diamond', 1)
SE =
Flat STREL object containing 5 neighbors.
Neighborhood:
0 1 0
1 1 1
0 1 0
15. Basics of Image Processing using MATLAB
>> SE = strel('square',3)
SE =
Flat STREL object containing 9 neighbors.
Neighborhood:
1 1 1
1 1 1
1 1 1
Check out help on strel for
>> SE = strel('line', 7, 45) various combinations
SE =
Flat STREL object containing 5 neighbors.
Neighborhood:
0 0 0 0 1
0 0 0 1 0
0 0 1 0 0
0 1 0 0 0
1 0 0 0 0
16. Basics of Image Processing using MATLAB
Dilation does not necessarily mean dilation of the holes also. The holes get
contracted as shown above.
Also try image erosion. Use MATLAB’s help.
17. Basics of Image Processing using MATLAB
Continuing with The Algorithm
When the dilated image of the character us subtracted from the original we get
something like…
Next we create such images for all the characters that we
want to recognize. (For all those individual character
images in the folder)
>> N = imread ('.char recogN.bmp');
>> SE = strel('square',3);
>> N1 = imdilate(N,SE);
>> N2 = N1 - N;
>> figure,imshow(N2)
18. Basics of Image Processing using MATLAB
Continuing with The Algorithm
Function, bwhitmiss is employed to check if a particular character is present in
the given image.
bwhitmiss(BW1,SE1,SE2) performs the hit‐miss operation defined by the
structuring elements SE1 and SE2. The hit‐miss operation preserves pixels
whose neighborhoods match the shape of SE1 and don't match the shape of SE2.
If the matrix returned by bwhitmiss contains non zero elements, then the
character is found in the image.
>> if ~isempty(nonzeros(bwhitmiss(im,N,N2)))
disp('N');
end
Also note the use of functions isempty and nonzeros
You can now use charrec.m to recognize few characters in a crude way.
19. Basics of Image Processing using MATLAB
Image Segmentation
Global Thresholding Method
>> im=imread('automata.jpg');
>> im_gray=rgb2gray(im);
% im2bw converts grayscale image to binary
% image using a global threshold
>> bw1=im2bw(im_gray);
>> bw2=im2bw(im_gray, threshold);
% if no threshold is specified, it uses a
% function graythresh to calculate the
% threshold. Otsu’s method is implemented in
% graythresh function.
20. Basics of Image Processing using MATLAB
Image Segmentation
Global Thresholding Method
Disadvantage is when there are multiple colors for objects and backgrounds.
Result with global thresholding – one of the blocks is lost
21. Basics of Image Processing using MATLAB
Image Segmentation
Local Thresholding Method: Niblack’s Method
>> im=imread('blocks.jpg');
255 if I( x, y ) T ( x, y ) >> im_gray=rgb2gray(im);
R( x, y ) 100 if I( x, y ) T ( x, y ) >> imt=niblack(im_gray,0.5,201);
>> figure,imshow(imt,[])
0
otherwise
% Here k=0.5 and N=201(preferably odd)
T ( x, y) N k N
% think about effects of values of N on
% processing time and k on thresholding
% level
k and N are to be empirically determined
22. Basics of Image Processing using MATLAB
Image Segmentation
Local Thresholding Method: Niblack’s Method
% Since our objects of interest are white pixels,
% we will consider those equal to 255
>> imwhite=(imt==255);
% observe how the above command works. It checks
% pixel by pixel for the condition and returns a
% matrix
>> figure, imshow(imwhite)
% Now we need to clear the noisier regions. We
% use an erosion process followed by reconstruction
>> im_eroded=imerode(imwhite,strel('square',51));
>> im_recon=imreconstruct(im_eroded,imwhite);
>> figure, imshow(im_recon)
23. Basics of Image Processing using MATLAB
Connected Components
% You can see pixels connected to each other
% form objects in the image. These are
% called connected components. Read more
% about 4-connectivity and 8-connectivity
% Label the connected components i.e. assign
% a particular number as pixel value to one CC
>> [bw_labelled num]=bwlabel(im_recon);
>> figure,imshow(bw_labelled,[])
% We can use regionprops() to extract some
% properties of the CCs
>> areas = regionprops(bw_labelled,'Area')
% Here areas is a struct variable
% Try experimenting with other properties and
% explore what property can be used to
% distinguish between CCs
24. Basics of Image Processing using MATLAB
% We will convert to struct to a normal array for easy operation
>> areas1=[];
>> for i=1:length(areas)
areas1=[areas1; areas(i,1).Area];
end
>> areas1
areas1 =
415711
26440
10350
8630
17971
8282
5243
% We are interested in objects (the squares) with area in range 8000-
% 9000
>> index=find(areas1>8000 & areas1<9000);
>> finalimg=zeros(size(bw_labelled));
>> for i=1:length(index)
finalimg=finalimg+(bw_labelled==index(i));
end
25. Basics of Image Processing using MATLAB
>> figure,imshow(finalimg,[])
This was again a very crude way, since we are depending only on value of
area which might not remain constant if camera changes position.
Most of the times the standard features available with regionprops() is
not sufficient. We will have to write our own code to extract features.
Also we used hard thresholds for areas to classify CCs. Again most of the
times, this is not followed. Classifiers using Pattern Recognition techniques
are employed.
26. Basics of Image Processing using MATLAB
Few Other Stuff
You can try
Edge detection
>> im=imread('ouch.jpg');
>> im_gray=rgb2gray(im);
>> imedge=edge(im_gray,'canny',[0.1 0.2]);
>> figure,imshow(imedge)
% Try different edge operators and
% threshold levels
and
Removing Noise By Median Filtering
(MATLAB Help)
There is more to learn in Image Processing. All the Best