Digital image processing is the use of a digital computer to process digital images through an algorithm.[1][2] As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems. The generation and development of digital image processing are mainly affected by three factors: first, the development of computers; second, the development of mathematics (especially the creation and improvement of discrete mathematics theory); third, the demand for a wide range of applications in environment, agriculture, military, industry and medical science has increased.
Intze Overhead Water Tank Design by Working Stress - IS Method.pdf
DIGITAL_SIGNAL_AND_IMAGE_PROCESSING_USIN.pptx
1. DIGITAL SIGNAL AND IMAGE
PROCESSING USING MATLAB
Compiled by
Prof.Ms.V.Ezhilya
2. MATLAB - INTRODUCTION
• It stands for MATrix LABoratory
• It is developed by The Mathworks Inc.
• It is an interactive, integrated, environment
• It is a high level programming language
• Many applications‐ specific toolboxes available
• Can be converted into C code via MATLAB compiler
for better efficiency
3. DEFINITIONS
• Signal
▫ Signal is a physical quantity that varies with time,
frequency or any other independent variable.
4. IMAGE
• A 2-D physical likeness or representation of a
person, animal, or thing, photographed, painted,
sculptured, or otherwise made visible.
5. SIGNAL PROCESSING
Signal processing is an enabling technology that
encompasses the fundamental theory,
applications, algortihms, and implementations
of processing or transferring information
contained in many different physical, symbolic,
or abstract formats broadly designated as
signals.
7. DIGITAL SIGNAL PROCESSING
• Digital signal processing (DSP) is the numerical
manipulation of signals, usually with the
intention to measure, filter, produce or
compress continuous analog signals.
• APPLICATIONS:
▫ Speech Processing - audio
▫ RADAR
▫ SONAR
▫ Bio-medical etc.,
8. MATLAB NOTATIONS
• xlabel(‘time’); % label the x-axis with time
• ylabel(‘amplitude’); % label the y-axis with amplitude
• title(‘xxxxx’); % put a title on the plot
• input(‘’); % enter I/P value
• clear; % clears the workspace, all
variables are removed.
• clear all; % clears all variables and
functions from work space.
9. MATLAB NOTATIONS – Contd.
• clc; % clears command window,
command history is lost.
• plot(x,y) % plot continuous time signal.
• subplot % breaks fig. window.
• Stem % to plot discrete time sequence
• axis tight; % set tight scale on axes.
• zeros(m,n) % returns an m by n matrix of
zeros.
10. MATLAB NOTATIONS – Contd.
• x=0:.1:20; % create vector x
• b=[2;3;5]; % create vector b
• disp % display array
• length(x) % gives length of vector x.
• conv % perform convolution
• sin % give sine wave
• cos %give cosine wave
• fft & ifft %find dft& idft values
11. GENERATION OF SIGNALS
% To plot the Sine Function
clc;
clear all;
x=0:1:40;
y=10*sin(2*pi*x/15);
subplot(2,1,1);
plot(x,y); %CT
title('CT sine wave');
grid;
subplot(2,1,2);
stem(x,y); %DT
title('DT sine wave');
grid;
14. DIGITAL IMAGE PROCESSING
• Digital image processing is the use of computer
algorithms to perform image processing on digital images.
• STEPS:
Image acquisition,
Image enhancement,
Image restoration,
Color image processing,
Wavelets and Multiresolution processing,
Compression,
Morphological processing,
Segmentation, Representation with description
Object recognition
15. DIP USING MATLAB
• A digital image is composed of a two or three
dimensional matrix of pixels.
• Individual pixels contain a number or numbers
representing what grayscale or color value is
assigned to it.
26. High level image understanding
• To imitate human cognition according to the
information contained in the image.
• Data represent knowledge about the image
content, and are often in symbolic form.
• Data representation is specific to the high-level
goal.
27. High level image understanding
• What are the high-level components?
• What tasks can be achieved?
Landmarks
(bifurcation/cros
sover)
Traces
(vessel
centerlines)
32. IMAGE PROPERTIES - HISTOGRAM
• A histogram is a graph drawn between the pixel
values ranging from 0 – 265 and the total
number of pixels in an image.
• In image processing it is used to show how many
values of pixels are present in an image.
• Histograms can be very useful in determining
which pixel values are important in an image.
35. FILTERS
• A filter, or convolution kernel - an algorithm for
modifying a pixel value.
• This could entail blurring, deblurring, locating
certain features within an image, etc.,
• LPF: It blur high frequency areas of images.
• This can sometimes be useful when attempting
to remove unwanted noise from an image.
38. MORPHOLOGICAL PROCESSING
• Processing an image based on its shape
• Types:
▫ Erosion: Removing pixel values from an Image
▫ Dilation: Adding pixel values to an Image
41. CONTACT
Ms. V.Ezhilya, M.E.,
Assistant Professor
VSA GI – Salem
Feedback & Suggestions are welcome
Ph: 7339000588
E-mail : ezhilyavenkat@gmail.com
Blog : ezhilyavenkat.blogspot.in
