Introduction
Lecturer: Dr. Hossam Hassan Email : [email protected]
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