Image processing using matlab

G.KARTHIGAM.SC INFO TECH
DEPARTMENT OF CS &IT
NADAR SARASWATHI COLLEGE OF
ARTS AND SCIENCE,THENI

 Matlab is optimised for operating on
matrices
 Images are matrices!
 Many useful built-in functions in the
Matlab Image Processing Toolbox
 Very easy to write your own image
processing functions
Image Processing using Matlab
Sumitha Balasuriya 2

>> I=imread('mandrill.bmp','bmp'); % load image
>> image(I) % display image
>> whos I
Name Size Bytes Class
I 512x512x3 786432 uint8 array
Grand total is 786432 elements using 786432 bytes
image filename
as a string
image format
as a stringMatrix with
image data
Dimensions of I (red, green
and blue intensity information)
Matlab can only perform
arithmetic operations on
data with class double!
Display the left
half of the
mandrill image

 Images are just an array of numbers
>> I % ctrl+c to halt output!
 Intensity of each pixel is represented by the pixel
element’s value in the red, green and blue matrices
>> I(1,1,:) % RGB values of element (1,1)
ans(:,:,1) =
135
ans(:,:,2) =
97
ans(:,:,3) =
33
Images where the pixel value in the image
represents the intensity of the pixel are called
intensity images.
Red
Green
Blue

 An indexed image is where the pixel values are indices to elements in a
colour map or colour lookup table.
 The colour map will contain entries corresponding to red, green and
blue intensities for each index in the image.
>> jet(20) % Generate a jet colourmap for 20 indices
ans =
0 0 0.6000
0 0 0.8000
0 0 1.0000
0 0.2000 1.0000
0 0.4000 1.0000
0 0.6000 1.0000
0 0.8000 1.0000
0 1.0000 1.0000
0.2000 1.0000 0.8000
0.4000 1.0000 0.6000
0.6000 1.0000 0.4000
0.8000 1.0000 0.2000
1.0000 1.0000 0
1.0000 0.8000 0
1.0000 0.6000 0
1.0000 0.4000 0
1.0000 0.2000 0
1.0000 0 0
0.8000 0 0
0.6000 0 0 Image Processing using Matlab
RGB Entry for index value 3
3 4 7 3 6 1 9 8 9 1 2
5 6 14 4 2 5 6 1 4 5
2 8 9 4 2 13 7 8 4 5
5 1 11 5 6 4 1 7 4 4
1 9 5 6 5 5 1 4 4 6 5
5 9 2 1 11 1 3 6 1 9
7 6 8 18 1 8 1 9 1 3 3
9 2 3 7 2 9 8 1 6 6 4
7 8 6 7 4 15 8 2 1 3
7 5 10 8 4 10 4 3 6 4
Values can range
from 0.0 to 1.0
Red, green and blue intensities of
the nearest index in the colourmap
are used to display the image.

>> I2=I(:,:,2); % green values of I
>> image(I2)
>> colorbar % display colourmap
Matlab considers I2 as an indexed image as it doesn’t
contain entries for red, green and blue entries
Index
Associated
color
Colour
Lookup
Table

 change colourmap
>> colormap(gray)
 scale colourmap
>> imagesc(I2)
Type >>help graph3d to get a list of built-in
colourmaps. Experiment with different
built-in colourmaps.
Define your own colourmap mymap by
creating a matrix (size m x 3 ) with red,
green, blue entries. Display an image using
your colourmap.
Red =1.0,
Green = 1.0,
Blue =1.0,
corresponds to
index 64
Red =1.0,
Green = 1.0,
Blue =1.0,
corresponds to
index 255
Red =0.0,
Green = 0.0,
Blue = 0.0,
corresponds to
index 1
Red =0.0,
Green = 0.0,
Blue = 0.0,
corresponds to
index 0

>> axis image % plot fits to data
>> h=axes('position', [0 0 0.5 0.5]);
>> axes(h);
>> imagesc(I2)
Investigate axis and axes
functions using Matlab’s help

 Frequency of the intensity values of the
image
 Quantise frequency into intervals (called
bins)
 (Un-normalised) probability density
function of image intensities

>>hist(reshape(double(Lena(:,:,2)),[512*512
1]),50)
Convert image into a 262144 by
1 distribution of values
Histogram
function
Number of bins
Histogram equalisation works by equitably distributing the pixels among the
histogram bins. Histogram equalise the green channel of the Lena image
using Matlab’s histeq function. Compare the equalised image with the
original. Display the histogram of the equalised image. The number of pixels
in each bin should be approximately equal.
Generate the histograms of the green channel of the Lena image using the
following number of bins : 10, 20, 50, 100, 200, 500, 1000

>>surf(double(imresize(Lena(:,:,2),[50 50])))
Remember to reduce
size of image!
Use Matlab’s built-in mesh and
shading surface visualisation
functions
Change type to
double precision

 Convert image to grayscale
>>Igray=rgb2gray(I);
 Resize image
>>Ismall=imresize(I,[100 100], 'bilinear');
 Rotate image
>>I90=imrotate(I,90);

Convert polar coordinates to
cartesian coordinates
>>pol2cart(rho,theta)
Check if a variable is null
>>isempty(I)
Trigonometric functions
sin, cos, tan
Convert polar coordinates to
cartesian coordinates
>>cart2pol(x,y)
Find indices and elements in a
matrix
>>[X,Y]=find(I>100)
Fast Fourier Transform
Get size of matrix
>>size(I)
Change the dimensions of a
matrix
>>reshape(rand(10,10),[100 1])
Discrete Cosine Transform
Add elements of a Matrix
(columnwise addition in matrices)
>>sum(I)
Exponentials and Logarithms
exp
log
log10
fft2(I)
dct(I)

Bit of theory! Convolution of two functions f(x) and
g(x)
Discrete image processing 2D form
( ) ( ) ( ) ( ) ( )h x f x g x f r g x r dr


   
convolution
operator
Image Filter
(mask/kernel)
Support region
of filter where
g(x-r) is nonzero
Output
filtered image
1 1
( , ) ( , ) ( , )
height width
j i
H x y I i j M x i y j
 
   
Compute the convolution where
there are valid indices in the kernel

Write your own convolution function
myconv.m to perform a convolution.
It should accept two parameters – the
input matrix (image) and convolution
kernel, and output the filtered matrix.

1/9 1/9 1/9
1/9 1/9 1/9
1/9 1/9 1/9
i
j
Filter (M)
Image (I)
197 199 195 194 189 190 132 90 112 101
194 194 198 201 189 196 150 85 87 97
194 194 198 195 186 191 109 90 90 124
197 187 195 198 185 186 115 78 81 96
194 190 198 193 187 177 88 86 94 69
194 194 190 190 179 177 93 99 95 100
201 194 191 186 186 181 74 110 82 76
196 194 195 191 183 164 77 119 84 88
192 194 199 192 191 174 89 164 103 129
201 190 187 189 178 168 90 82 88 84
0 0 0 0 0 0 0 0 0 0
0 196 196 194 192 170 137 105 97 0
0 195 196 194 192 167 133 98 92 0
0 194 194 193 189 158 124 92 90 0
0 193 193 191 186 154 122 92 89 0
0 194 192 189 184 149 121 91 90 0
0 194 192 188 182 146 122 93 95 0
0 195 193 190 183 147 128 100 106 0
0 194 192 189 181 146 125 100 105 0
0 0 0 0 0 0 0 0 0 0
=
1 1
( , ) ( , ) ( , )
height width
j i
H x y I i j M x i y j
 
   
http://www.s2.chalmers.se/undergraduate/courses0203/ess060/PDFdocuments/ForScreen/Notes/Convolution.pdf

Horizontal slice from Mandrill image
0.01 0.08 0.24 0.34 0.24 0.08 0.01
1D Gaussian filter
 =
Filtered Signal

0.11 0.11 0.11
0.11 0.11 0.11
0.11 0.11 0.11
Arithmetic mean
filter (smoothing)
>>fspecial('average')
-0.17 -0.67 -0.17
-0.67 3.33 -0.67
-0.17 -0.67 -0.17
Laplacian (enhance edges)
>>fspecial('laplacian')
-0.17 -0.67 -0.17
-0.67 4.33 -0.67
-0.17 -0.67 -0.17
Sharpening filter
>>fspecial('unsharp')
0.01 0.08 0.01
0.08 0.62 0.08
0.01 0.08 0.01
Gaussian filter
(smoothing)
>>fspecial('gaussian')
Investigate the listed kernels in Matlab by
performing convolutions on the Mandrill and
Lena images. Study the effects of different
kernel sizes (3x3, 9x9, 25x25) on the output.
1 2 1
0 0 0
-1 -2 -1
1 0 -1
2 0 -2
1 0 -1
Sobel operators (edge detection in x
and y directions)
>>fspecial('sobel')
>>fspecial('sobel')’
The median filter is used for noise reduction. It works by
replacing a pixel value with the median of its neighbourhood
pixel values (vs the mean filter which uses the mean of the
neighbourhood pixel values). Apply Matlab’s median filter
function medfilt2 on the Mandrill and Lena images. Remember
to use different filter sizes (3x3, 9x9, 16x16).

 Generate useful filters for convolution
>>fspecial('gaussian',[kernel_height kernel_width],sigma)
 1D convolution
>>conv(signal,filter)
 2D convolution
>>conv2(double(I(:,:,2)),fspecial('gaussian‘,[kernel_height kernel_width]
,sigma),'valid')
Perform the convolution of an image using Gaussian
kernels with different sizes and standard deviations
and display the output images.
Border padding optionskernelimage

1) Type the code in this handout in Matlab and investigate the results.
2) Do the exercises in the notes.
3) Create a grating2d.m function that generates a 2D steerable spatial
frequency. Compute spatial frequencies with an amplitude =1 and
the following parameters
frequency = 1/50, 1/25, 1/10, 1/5 cycles per pixel,
phase= 0, pi/5, pi/4, pi/3, pi/2, pi
theta = 0, pi/5, pi/4, pi/3, pi/2, pi
The value for pi is returned by the in-built matlab function pi.
Display your gratings using the in-built gray colourmap. (figure 1)
3) Create a superposition of two or more gratings with different
frequencies and thetas and display the result. You can do this by
simply adding the images you generated with grating2d (figure 2)
frequency = (1/10 and 1/20), (1/20 and 1/30)
theta = (pi/2 and pi/5), (pi/10 and pi/2), (pi/2 and pi)
Make sure you examine combinations of different frequencies and
theta values. (figure 3).
Visualise the intensity surface of the outputs that you have
generated. (figure 4)
4) Write a matlab function that segments a greyscale image based on a
given threshold (i.e. display pixels values greater than the threshold
value, zero otherwise). The function should accept two inputs, the
image matrix and the threshold value, and output the thresholded
image matrix. (figure 5)
function H=grating2d(f,phi,theta,A)
% function to generate a 2D grating image
% f = frequency
% phi = phase
% theta = angle
% A = amplitude
% H=grating2d(f,phi,theta,A)
% size of grating
height=100;
width=100;
wr=2*pi*f; % angular frequency
wx=wr*cos(theta);
wy=wr*sin(theta);
for y=1:height
for x=1:width
H(x,y)=A*cos(wx*(x)+phi+wy*(y));
end
end
Figure 1 Figure 3Figure 2 Figure 4
Figure 5

Image processing using matlab

Recommended

More Related Content

What's hot (20)

Similar to Image processing using matlab (20)

More from SangeethaSasi1 (20)

Recently uploaded (20)

Image processing using matlab