Uniq Technologies

Image Processing using MATLAB

Introduction

• MATLAB is a high-level language and interactive environment for

numerical computation, visualization, and programming. Using MATLAB,

you can analyze data, develop algorithms, and create models and

applications

• MATLAB is intended primarily for numerical computing, an optional

toolbox allowing access to symbolic computing capabilities. An additional

package, Simulink, adds graphical multi-domain simulation and Model-

Based Design for dynamic and embedded systems

Matrices in Matlab

• Defining a matrix is similar to defining a vector. Creating a matrix is as easy

as making a vector, using semicolons (;) to separate the rows of a matrix

A = [ 1 2 3; 3 4 5; 6 7 8]

• You can also treat it like a row of column vectors

B = [ [1 2 3]' [4 5 6]' [7 8 9]']

Matrix Declaration and Initialization

Transpose of the matrix A C = A’

Let’s try the list of Functions yourself…

1. X = inv(A); inverse of a matrix

2. I = inv(A) * A; inverse is the identity matrix

3. eig(A); [v,e] = eig(A); diag(e);

Eigen values and the Eigen vectors.

Probability density functions:

Y = pdf(name , X, A, B, C);

Eg: p1 = pdf('Normal',-2:2, 0,1)

p2 = pdf('Poisson', 0:4, 1:5)

What is Matlab?

• Matlab is basically a high level language which has many specialized

toolboxes for making things easier for us

Assembly

High Level Languages such as

C, Pascal etc.

Matlab

Matlab tool

• Command Window

– type commands

• Current Directory

– View folders and m-files

• Workspace

– View program variables

– Double click on a variable

to see it in the Array Editor

• Command History

– view past commands

– save a whole session using diary

Use of M-File

Click to create a new

M-File

• Extension “.m”

• A text file containing script or function or program to run

• Go to , File New Script or Function

Use of M-File

If you include “;” at the

end of each statement,

result will not be shown

immediately

Project Deployment

Windows Application

JAVA Application

Dotnet Application

Matlab can be interface with object oriented programming

IMAGE PROCESSING

TOOLBOX

Image file process in MATLAB

MATLAB can import/export several image formats

BMP (Microsoft Windows Bitmap)

GIF (Graphics Interchange Files)

HDF (Hierarchical Data Format)

JPEG (Joint Photographic Experts Group)

PCX (Paintbrush)

PNG (Portable Network Graphics)

TIFF (Tagged Image File Format)

XWD (X Window Dump)

MATLAB can also load raw-data or other

types of image data

Data conversion types in MATLAB

Double (64-bit double-precision floating

point)

Single (32-bit single-precision floating

point)

Int32 (32-bit signed integer)

Int16 (16-bit signed integer)

Int8 (8-bit signed integer)

Uint32 (32-bit unsigned integer)

Uint16 (16-bit unsigned integer)

Uint8 (8-bit unsigned integer)

Image import and export

• Read and write images in Matlab

I=imread('cells.jpg');

imshow(I);

size(I);

ans = 479 600 3 (RGB image)

Igrey=rgb2gray(I); (RGB to gray)

imshow(Igrey);

imwrite(lgrey, 'cell_gray.tif', 'tiff');

Read images in Matlab from user directory

file= 'C:\\MATLAB\\VideoImage\\';

imageList = dir(fullfile(file,'*.jpg'));

Im= imread([file,imageList(4).name]);

Images and Matrices

How to build a matrix (or image)?

A = [ 1 2 3; 4 5 6; 7 8 9 ];

A = 1 2 3

4 5 6

7 8 9

B = zeros(3,3); B = 0 0 0

0 0 0

0 0 0

C = ones(3,3) ; C = 1 1 1

1 1 1

1 1 1

imshow(A) (imshow(A,[]) to get automatic pixel range)

Image Conversion

Im = imread('fr2.jpg');

Im2 = ones(size(Im));

Im3 = zeros(size(Im));

im4=rgb2gray(Im);

Image Function Image= rgb2hsv(Im); (RGB to HSV model)

Image= im2bw(Im,0.5); (Image to Binary)

• Image Noising :

Image= imnoise (Im,'salt & pepper');

Image= imnoise (Im,‘Gaussain');

Image Type Conversion

1. gray2ind(X, map); Convert grayscale or binary image to indexed image

2. ind2gray(X, map) ; Convert indexed image to grayscale image. Map-

color intensity “n” color values

3. ind2rgb(I, map) Convert indexed image to RGB image.

4. rgb2gray(I); Convert RGB image or colormap to grayscale

5. mat2gray(I, [amin, amax]); Convert matrix to grayscale image

6. im2bw(I, map, level); Convert image to binary image, based on

threshold

7. graythresh(I); Global image threshold using Otsu's method

8. multithresh(I, n); Multilevel image thresholds using Otsu ´s method

9. grayslice(I, v); Convert grayscale image to indexed image using

multilevel thresholding. vector ‘v’ values between 0 and 1

10. im2double(I) or im2double(BW) or im2double(X, index); Convert

image to double precision

Subplot

subplot - display multiple plots in the same window

subplop(nrows,ncols,plot_number)

code

x=0:.1:2*pi;subplot(2,2,1);plot(x,sin(x));

subplot(2,2,2);plot(x,cos(x));

subplot(2,2,3)plot(x,exp(-x));subplot(2,2,4);plot(peaks);

Subplot

Filtering

Function

Y= filter2(filter,image,shape)

Description

returns the part of Y specified by the shape parameter. Shape is a

string with one of these values:

'full' Returns the full two-dimensional correlation. In this case, Y is larger

than X

'same' (default) Returns the central part of the correlation. In this case, Y

is the same size as X

'valid' Returns only those parts of the correlation that are computed

without zero-padded edges. In this case, Y is smaller than X

Filtering code

• >> clear all

>> a=imread('cute01.tif');

>> imshow(a);

>> Fil=fspecial('average');% function fspecial is one(3,3)/9

>> aFilter=filter2(Fil,a);

>> imshow(aFilter/255);

Filtering image

RGB CHANNEL CONVERSION

a=imread('image2.jpg');

subplot(3,1,1);

imshow(a);

aa=rgb2gray(a);

b=imnoise(aa,'salt & pepper',0.02);

subplot(3,1,2);

imshow(b);

bb=filter2(fspecial('average', [3 3], 255));

c=medfilt(b,[3 3]);

subplot(3,1,3);

imshow(c);

Code

OUTPUT OF CHANNEL CONVERSION

EDGE DETECTION

Edge detection is the process of localizing pixel intensity transitions. The

edge detection have been used by object recognition, target tracking,

segmentation, and etc. Therefore, the edge detection is one of the most

important parts of image processing.

There mainly exists several edge detection methods (Sobel [1,2], Prewitt

[3], Roberts [4],Canny [5]).

CODE

a=imread('image2.jpg');

subplot(3,2,1);imshow(a);title('original image');

aa=rgb2gray(a);

b=edge(aa,'sobel');

subplot(3,2,2);imshow(b);title('sobel edge');

c=edge(aa,'canny');

subplot(3,2,3); imshow(c);title('canny edge');

d=edge(aa,'prewitt');

subplot(3,2,4);imshow(d);title('prewitt edge');

e=edge(aa,'roberts');

subplot(3,2,5);imshow(e);title('roberts');

EDGE DETUCTION OUTPUT

Functions in matlab

• Predefined functions

Matlab has a variety of pre-defined functions readily available for use

by simply using their names and providing the right number and type of

arguments. For example, the natural logarithm function (log) is readily

available:

» log(-5.2) ans = 1.6487 + 3.1416i

• User defined function

• Function[output arguments] = functionname[input arguments ]

ex: Function [a]=plot[x,y]

a=7;

y=sin(o.5:.1:50) ;

end

Graphical user interface

What is GUIDE?

• GUIDE is Matlab’s Graphics User Interface (GUI)

Design Environment GUIDE stores GUIs in two files, which are generated

the first time you save or run the GUI:– .fig file - contains a complete

description of the GUI figure layout and the components of the GUI •

Changes to this file are made in the Layout Editor – .m file - contains the

code that controls the GUI • You can program the callbacks in this file

using the M-file Editor

Creating a GUI

Typical stages of creating a GUI are:

1. Designing the GUI

2. Laying out the GUI

- Using the Layout Editor

3. Programming the GUI

- Writing callbacks in the M-file Editor

4. Saving and Running the GUI

Writing Callbacks

A callback is a sequence of commands that are execute when a graphics

object is activated

• Stored in the GUI’s M-file

• Is a property of a graphics object (e.g. CreateFnc, ButtonDwnFnc,Callback, DeleteFnc)

• Also called event handler in some programming languages A callback is usually made of the following stages:

1. Getting the handle of the object initiating the action (the objectprovides event / information / values)

2. Getting the handles of the objects being affected (the object that whose properties are to be changed)

3. Getting necessary information / values

4. Doing some calculations and processing

5. Setting relevant object properties to effect action

Writing Callbacks

function pushbutton1_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton1 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

[filename pathname]=uigetfile({'*.jpg';'*.tif'},'File Selector');

image=strcat(pathname, filename);

axes(handles.axes1);

imshow(image)

set(handles.edit2,'string',filename);

set(handles.edit1,'string',pathname);

Image browser

Push button handles

Slider value example

Call back function

function slider1_Callback(hObject, eventdata, handles)

a=get(handles.slider1,'Value');

x=0:0.1:50;

y=sin(x*a);

plot(handles.axes1,x,y)

Slider value

Increasing slider value

Property inspector

• To change the properties

of GUI

Queries & Clarifications

Happy Learning