Image processing Lecture 1

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.