Image processing Lecture 1
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Digital Image ProcessingELE-4707
Dr Hassan Ahmed
Introduction
Instructor Hassan AhmedTA Usman Tariq, Mohammad HanifLecture timings Tuesday (4.00-5.30) – room N5
Thursday (2:30-4:00) – room 211Lab Session Friday (10.00-1.00) – Lab-2A
Text books and notes1. R. C. Gonzalez and R. E. woods, “Digital Image
Processing”, 2nd edition, Pearson Education, Inc., 2002.
2. “Digital Image Processing using MATLAB”R. C. Gonzalez , R. E. Woods and S.L. EddinsPearson Education, Inc., 2004.
3. Class SlidesPrerequisites
1. Knowledge of probability and random variables, Vectors and Matrices.
2. Working knowledge of Matlab3. Signals and Systems course especially the
concepts of Convolution, Fourier Transform, filtering, etc.
Marking Scheme
• Sessional 1: ~10%• Sessional 2: ~15%• Surprise Quizzes: ~10%• Assignments ( written + programming) ~15%• Final: ~50%
Marking Scheme can change without any notice, during the semester in benefit of all the students
Assignments
• Assignments will have ~15% weight in the total marks.
• Assignments may be programming assignments.
• The deadline for the submission of assignment will be given
with the assignment.
• Assignments submitted after the deadline will not be
accepted and will carry ZERO MARKS.
• Cheated assignments will get ZERO MARKS.
Motivation
Image processing is used for two somewhat different
purposes:
• improving the visual appearance of images (pictorial
information ) to a human viewer, and
• Preparing (processing) images for measurement of the
features and structures present.
– autonomous machine perception.
The techniques that are appropriate for each of these tasks
are not always the same, but there is considerable overlap.
This course covers methods that are used for both
purposes.
What Is Digital Image Processing
• The field of digital image processing refers to processing
digital images by means of a digital computer.
• A digital image can be defined as a two-dimensional function,
f (x, y), where
x and y are spatial coordinates, and f intensity or gray level of
the image at that point.
• Image Processing image in -> image out
• Image Analysis image in -> measurements out
• Image Understanding image in -> high-level description out
These are somewhat artificial boundary
Classification of DIP and Computer Vision Processes
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• Low-level process: (DIP)
– Primitive operations where inputs and outputs are images Major
functions: image pre-processing like noise reduction, contrast
enhancement, image sharpening, etc.
• Mid-level process (DIP and Computer Vision and Pattern Recognition)
– Inputs are images, outputs are attributes (e.g., edges). major
functions: segmentation, description, classification / recognition of
objects
• High-level process (Computer Vision)
– make sense of an ensemble of recognized objects; perform the
cognitive functions normally associated with vision
Example of DIP
Examples:
(a) Image of cell corrupted by noise
(b) The result of averaging
(c) Image of Martian surface corrupted by interference in transmission
(d) The result of computer processing
(e) Poorly exposed x-ray image
(f) The result from contrast and edge enhancement
(g) Image blurred by motion
(h) The result of de-blurring
Poorly illuminated CCTV image and the result of histogram equalisation.
Finding the outline and shape of image objects, e.g. character recognition.
Satellite imagery in false colour and infrared to track vegetation changes
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Face detection
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Face Tracking
Applications of Digital Image Processing (DIP) include:
1)Biological Research: e.g. DNA typing and matching; automatic counting and classification of cell structures in bone and tissue.
2) Defence and Intelligence: e.g. Reconnaissance photo-interpretation of objects in satellite images; target acquisition and missile guidance.
3) Document Processing: e.g. Scanning, archiving and transmission (fax); automatic detection and recognition of printed text (postal sorting office, tax return processing, banking cheques).
4) Factory Automation: e.g. Visual quality inspection, defect detection and process monitoring.
5) Law Enforcement Forensics: e.g. Photo-ID kits, criminal photo-search, automatic fingerprint matching, DNA matching and fibre analysis.
6) Materials Research: e.g. Automatic counting and classification of object characteristics such as impurities and grain size; surface and structural defect analysis (x-ray analysis for aircraft wing cracks)
7) Photography: e.g. Retouching defects, altering colours, zooming; adding and subtracting objects to a scene; special effects such as blending and warping.
8) Publishing: e.g. Layout composition, inserting pictures, generating graphics; colour separation for 4-colour printing (cyan, magenta, yellow and black)
9) Remote Sensing: e.g. Land cover analysis (water, roads, cities and cultivation), vegetation features (water content and temperature) and crop yield analysis; 3-D terrain rendering from satellite or aircraft data (road and dam planning); fire and smoke detection.
10) Space exploration and Astronomy: e.g. Image compression for transmission, correction of detector deficiency; automatic satellite navigation and altitude control using star positions.
11) Video and Film Special Effects: Animation, dangerous stunts (explosions) and special effects (Star Wars).
12) Other examples/areas ??
More examples can be found in Gonzalez’s Book
• The imaging machines can cover almost the entire EM
spectrum, ranging from gamma to radio waves. These include
– Gamma ray images
– x-ray band images
– ultra-violet band images
– visual light and infra-red images
– Imaging based on micro-waves and radio waves
• Non-EM band Imaging
– Acoustic and ultrasonic images (Geological application, ??)
– Transmission Electron Microscopy
– Computer-generated synthetic images
• Thus, digital image processing encompasses a wide and
varied field of applications.
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EM Spectrum
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Image Processing Steps
Imaging Image acquisition
Digitization, quantization and compression
Enhancement and restoration
Image segmentation
Feature selection/extraction
Image representation
Image interpretation
Physical world
Physical action
Image Processing
Imaging Analysis (Computer Vision and Pattern recognition)
Image understanding (Computer Vision and Pattern recognition)
Image acquisition is the first process shown in the previous
slide
• Note that acquisition could be as simple as being given an
image that is already in digital form. Generally, the image
acquisition stage involves pre-processing, such as scaling
etc.
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Image Acquisition
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Image description
f (x,y): intensity/brightness of the image at spatial coordinates (x,y)
0< f (x,y)<∞ and determined by 2 factors:
illumination component i(x,y): amount of source light incident
reflectance component r(x,y): amount of light reflected by objects
f (x,y) = i(x,y) r(x,y)
where
0< i(x,y)<∞: determined by the light source
0< r(x,y)<1: determined by the characteristics of objects
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Sampling and Quantization
sampling sampling
quantiza
tion
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Sampling and Quantization
Sampling: Digitization of the spatial coordinates (x,y)
Quantization: Digitization in amplitude (also called gray-level quantization)
8 bit quantization: 28 = 256 gray levels (0: black, 255: white)
Binary (1 bit quantization): 2 gray levels (0: black, 1: white)
Commonly used number of samples (resolution)
Digital still cameras: 640x480, 1024x1024, up to 4064 x 2704
Digital video cameras: 640x480 at 30 frames/second 1920x1080 at 60 f/s (HDTV)
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Sampling and Quantization
An M x N digital image is expressed as
)1,1(...)1,1()0,1(
......
......
......
)1,1(...)1,1()0,1(
)1,0(...)1,0()0,0(
NMfMfMf
Nfff
Nfff
N : No of Columns
M : No of Rows
Row
s
Columns
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Digital Images
Digital images are 2D arrays (matrices) of numbers:
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Sampling
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Sampling
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Effect of Sampling and Quantization
250 x 210 samples 256 gray levels
125 x 105 samples
50 x 42 samples
25 x 21 samples
16 gray levels
8 gray levels
4 gray levels
Binary image
Step in Image Processing
• Image enhancement is the simplest and most appealing
areas of digital image processing. Basically, the idea behind
enhancement techniques is to bring out detail that is
obscured, or simply to highlight certain features of interest
in an image. A familiar example of enhancement is when
we increase the contrast of an image because “it looks
better.”
• Image restoration is an area that also deals with improving
the appearance of an image. However, unlike
enhancement, which is subjective, image restoration is
objective, in the sense that restoration techniques tend to
be based on mathematical or probabilistic models of image
degradation. Enhancement, on the other hand, is based on
human subjective preferences regarding what constitutes a
“good” enhancement result.
• Color image processing is an area that has been gaining in
importance because of the significant increase in the use of
digital images over the Internet.
• Compression, as the name implies, deals with techniques
for reducing the storage required to save an image, or the
bandwidth required to transmit it.
• Segmentation procedures partition an image into its
constituent parts or objects. In general, autonomous
segmentation is one of the most difficult tasks in digital
image processing
• Representation and description almost always follow the
output of a segmentation stage
• Recognition is the process that assigns a label (e.g.,
“vehicle”) to an object based on its descriptors.
Reading Assignment for this week
• Chapters 1 and 2 of “Digital Image Processing” by
Gonzalez.
• Chapters 2 of “Digital Image Processing using MATLAB” by
Gonzalez.
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