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Digital Image Processing
EEE415

Introduction

Instructor Tariq Mahmood

Lecture timings Monday (11:30-1.00) – Room 106

Wednesday (14:30-16:00) – Room 106

Lab Session Wednesday(11:30-14:30) – Control and DSP Lab

Text books and notes

R. C. Gonzalez and R. E. woods, “Digital Image Processing”, 2nd edition, Pearson Education, Inc.

R. C. Gonzalez and R. E. woods, “Digital Image Processing”, 3rd edition, Pearson Education, Inc.

R. C. Gonzalez , R. E. Woods and S.L. Eddins, “Digital Image Processing using MATLAB”, Pearson Education, Inc., 2004.

Class Slides

Prerequisites

Knowledge of probability and random variables, Vectors and Matrices.

Working knowledge of Matlab

Signals and Systems course especially the concepts of Convolution, Fourier Transform, filtering, etc.

Marking Scheme

Theory:

Sessional 1: ~10%
Sessional 2: ~15%
Surprise Quizzes: ~10%
Assignments ( written + programming) ~5%
Project ~10%
Final: ~50%

Lab:

Lab Work: ~25%
Lab Mid: ~25%
Lab 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

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

(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

Face detection

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.

EM Spectrum

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.

Image Acquisition

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

Sampling and Quantization

sampling

quantization

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)

Sampling and Quantization

An M x N digital image is expressed as

N : No of Columns

M : No of Rows

Rows

Columns

Digital Images

Digital images are 2D arrays (matrices) of numbers:

Sampling

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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