Introduction

Lecturer: Dr. Hossam Hassan Email : hossameldin.hassan@eng.asu.edu.eg

Computers and Systems Engineering

Essential Books

1. Digital Image Processing – Rafael Gonzalez and Richard Woods, Third Edition, Prenhall, 2008

2. Digital Image Processing using MATLAB – Rafael Gonzalez, Richard Woods and Steven Eddins, Prenhall, 2008

3. Image processing, analysis and machine vision – Milan Sonka, Vaclav Hlavac and Roger Boyle, Third edition, Thomson

Learning, London, 2008

Course Contents

• Introduction

• Digital Image Fundamentals

• Image Enhancement in the Spatial Domain

• Image Enhancement in the Frequency Domain

• Edge Detection

• Image Segmentation

• Representation and Description

• Introduction to Object Recognition

3

Grading System

• Final examination 70%

• Midterm examination + Assignment/Quiz/Report 30%

• Warnings:

– A quiz may be given without being informed before.

– Copying assignment is prohibited.

– Delay of submission influences on marks

4

Overview

• Early days of computing, data was numerical.

• Later, textual data became more common.

• Today, many other forms of data: voice, music,

speech, images, computer graphics, etc.

• Each of these types of data are signals.

• Loosely defined, a signal is a function that conveys information.

Relationship of Signal Processing to other fields

• As long as people have tried to send or receive

through electronic media : telegraphs,

telephones, television, radar, etc. there has been

the realization that these signals may be affected

by the system used to acquire, transmit, or

process them.

• Sometimes, these systems are imperfect and introduce noise, distortion, or other artifacts.

• Understanding the effects these systems have and

finding ways to correct them is the fundamental of

signal processing.

• Sometimes, these signals are specific messages

that we create and send to someone else (e.g.,

telegraph, telephone, television, digital networking,

etc.).

• That is, we specifically introduce the information

content into the signal and hope to extract it out later.

Acquiring

Natural

Image

Enhance

Picture

Compress

for

Transmission

Encode and

Transmit over

Digital network

Transmitted

Codes of

Image Decode Decompress Display

Sender

Recipient

Concerned fields:

• Digital Communication

• Compression

• Speech Synthesis and Recognition

• Computer Graphics

• Image Processing

• Computer Vision

What is Image Processing?

• Image processing is a subclass of signal

processing concerned specifically with pictures.

• Improve image quality for human perception and/or computer interpretation.

Several fields deal with images

• Computer Graphics : the creation of images.

• Image Processing : the enhancement or other

manipulation of the image – the result of which is

usually another images.

• Computer Vision: the analysis of image content.

Several fields deal with images

2 Principal application areas

1. Improvement of pictorial information for human

interpretation.

2. Processing of image data for storage,

transmission, and representation for autonomous machine perception.

Pictorial: of or expressed in pictures; illustrated

Ex. of fields that use DIP

• Categorized by image sources

– Radiation from the Electromagnetic spectrum

– Acoustic

– Ultrasonic

– Electronic (in the form of electron beams used

in electron microscopy)

– Computer (synthetic images used for modeling and visualization)

Gamma-Ray Imaging

• Nuclear Image

– (a) Bone scan

– (b) PET (Positron emission

tomography) image

• Astronomical

Observations.

– (c) Cygnus Loop

– Nuclear Reaction

– (d) Gamma radiation from a reactor valve

X-ray Imaging

• Medical diagnostics

– (a) chest X-ray (familiar)

– (b) aortic angiogram

– (c) head CT

• Industrial imaging

– (d) Circuit board

• Astronomy – (e) Cygnus Loop

Imaging in Visible and Infrared Bands

• Astronomy

• Light microscopy

• Pharmaceuticals – (a) taxol (anticancer agent)

– (b) Cholesterol

• Micro-inspection to materials characterization – (c) Microprocessor

– (d) Nickel oxide thin film

– (e) Surface of audio CD

– (f) Organic superconductor

Remote sensing

Remote Sensing: Weather Observations

Imaging in Radio Band

Ultrasound Imaging

Generated images by computer

3 types of computerized process

Image Analysis Examples: reading bar coded tags or as

sophisticated as identifying a person from his/her face.

Fundamental steps

Image Acquisition:

Camera

Frame Grabber

Image Enhancement

Image Restoration

Color Image Processing

Wavelets

Compression

Morphological processing

Image Segmentation

Representation & Description

Representation & Description

Recognition & Interpretation

Knowledge base

Human and Computer Vision

Simple questions

43

What is Vision?

Recognize objects

– people we know

– things we own

• Locate objects in space

– to pick them up

• Track objects in motion

– catching a baseball

– avoiding collisions with cars on the road

• Recognize actions

– walking, running, pushing

Vision is

• Deceivingly easy

• Deceptive

• Computationally demanding

• Critical to many applications

Vision is Deceivingly Easy

•We see effortlessly

– seeing seems simpler than “thinking”

– we can all “see” but only select gifted people can

solve “hard” problems like chess

– we use nearly 70% of our brains for visual

perception!

• All “creatures” see

– frogs “see”

– birds “see”

– snakes “see”

but they do not see alike

Vision is Deceptive

Vision is an exceptionally strong sensation

– vision is immediate

– we perceive the visual world as external to

ourselves, but it is a reconstruction within our brains

– we regard how we see as reflecting the world “as

it is;” but human vision is

• subject to illusions

• quantitatively imprecise

• limited to a narrow range of frequencies of radiation

• passive

Some Illusion

Some Illusion

Some Illusion

Some Illusion

Human Vision is Passive

• It relies on external energy sources (sunlight,

light bulbs, fires) providing light that reflects

off of objects to our eyes

• Vision systems can be “active” - carry their

own energy sources – Radars

– Bat acoustic imaging systems

Spectral Limitation of Human Vision

• We “see” only a small part of the energy

spectrum of sunlight

– we don’t see ultraviolet or lower frequencies of

light

– we don’t see infrared or higher frequencies of light

– we see less than .1% of the energy that reaches

our eyes

• But objects in the world reflect and emit energy in

these and other parts of the spectrum

Structure of the Human Eye

Structure of the Human Eye

Lens & Retina

Receptors

Cones

Rods

Contrast sensitivity

Weber ratio

Simultaneous contrast

Which small square is the darkest one ?

Signals

Time-Varying Signals

Spatially-Varying Signals

Spatiotemporal Signals

Video Signal!

Types of Signals

Analog & Digital

Sampling

Quantization

Digital Image Representation

Digital Image Representation

Digital Image Representation

Example of Digital Image

Light-intensity function

Illumination and Reflectance

Illumination and Reflectance

Gray level

Color Perception

• Color is an important part of our visual experience.

• We distinguish only 100 levels of grays but hundreds of

thousands of colors.

• Color detection is important to computer vision;

• Object recognition is easier

• Underutilized because more processing is required, hard

to publish

Color Perception of Reflection

Color Models

• Color Models are useful for driving hardware that generates

or captures images

• Monitors, TVs, video cameras

• Color printers

• Since color sensation can be reproduced by combination of

pure colors, it is simpler to use phosphors and CCD

(charge-couple device) elements that have sharp and

narrow spectra rather than combine overlapping spectra.

• Color models describe in what proportion to combine

these spectra to produce different color impressions.

Additive Color Models

• In monitors, 3 electron beams illuminate phosphors of 3

colors that act as additive light sources.

• The powers of these beams are controlled by the

components of colors described by the R,G,B model

Color Models (RGB Cube)

Number of bits

Resolution

Checkerboard effect

False contouring

Nonuniform sampling

Example

Example

Nonuniform quantization

Image Formation

Lens-less Imaging Systems - Pinhole

Optics • Projects images

– without lens

– with infinite depth of field

• Smaller the pinhole

– better the focus

– less the light energy from any single point

• Good for tracking solar eclipses

Pinhole Camera (Cont…)

Distant Objects are Smaller

Pinhole Camera (Cont…)

Bigger Hole-More Blurred Images

Lenses Collect More Lights

• With a lens, diverging rays from a scene point are

converged back to an image point

Lens Equation

n: Lens’ Refractive Index

hI: Image Height

ho: Object Height

The negative values for image height indicate that the image is an inverted

image…

Thin Lens

• relates the distance between the scene point being viewed

and the lens to the distance between the lens and the point’s

image (where the rays from that point are brought into focus

by the lens)

• Let M be a point being viewed, p is the distance of M from the

lens along the optical axis.

• The thin lens focuses all the rays from M onto the same point,

the image point m at distance q from the lens.

Thin Lens Equation

• m can be determined by intersecting two known rays

– MQ is parallel to the optical axis, so it must be refracted to pass

through F.

– MO passes through the lens center, so it is not bent.

• Note two pairs of similar triangles – MSO and Osm (yellow)

– OQF and Fsm (green)

Thin Lens Equation

• As p gets large, q approaches f

• As q approaches f, p approaches infinity

Field of View

• As f gets smaller, image becomes more wide angle (more

world points project onto the finite image plane).

• As f gets larger, image becomes more telescopic (smaller

part of the world projects onto the finite image plane)

According to that MODEL?

Vanishing Point?

Vanishing Point – TWO Points Perspective

vx vy

Optical Power and Accommodation

Optical power of a lens - how strongly the lens bends the

incoming rays

– Short focal length lens bends rays significantly

– It images a point source at infinity (large p) at distance f

behind the lens. The smaller f, the more the rays must be

bent to bring them into focus sooner.

– Optical power is 1/f, with f measured in meters. The unit is

called the diopter

– Human vision: when viewing faraway objects the distance

from the lens to the retina is 0.017m. So the optical power of

the eye is 58.8 diopters

Accommodation

How does the human eye bring nearby points into focus on the

retina? – by increasing the power of the lens

– muscles attached to the lens change its shape to change the lens

power

– accommodation: adjusting the focal length of the lens

– bringing points that are nearby into focus causes faraway

points to go out of focus

– depth-of-field: range of distances in focus

Accommodation

Physical cameras: mechanically change the distance between

the lens and the image plane