Biology for Computer Engineers Course Handout.pptx
CSE367 Lecture 1 image processing lecture
1. CSE367 DIGITAL IMAGE PROCESSING
LECTURE 1
INTRODUCTION & FUNDAMENTALS (PART 1)
Dr. Fatma Newagy
Associate Prof. of Communications Engineering
Fatma_newagy@eng.asu.edu.eg
2. Rules and Ethics
• No more 10 minutes late
• Use chat window for questions
• Mute students when not participating
• Good behavior and participation is a must
• Turn on your camera if needed
• Send your feedback by email after each week with your
suggestions
3. Assessment Weights and Schedule
Assignments Weeks 3, 9
Quizzes Weeks 4, 11
Mid-Term Examination Week 8
Project Week 14
Final Exam End of semester
Attendance and Participation 5%
Assignments / project 10%
Quizzes 10%
Mid-Term Exam 25%
Lab Reports 10%
Final Exam 40%
Total 100%
Any copied
document
(assignments,
reports, projects,…)
from internet/
student
Will get negative
mark
4. Article Reading and Project
• Article Reading and Project
• Medical image analysis
• Face, fingerprint, and other object recognition
• Image and/or video compression
• Image segmentation and/or denoising
• Digital image/video watermarking/steganography and
detection
• Whatever you’re interested …
5. Evaluation of article reading and project
• Evaluation of article reading and project
• Report
Article reading
— Submit a survey of the articles you read and the list of the
articles
Project
— Submit an article including introduction, methods, experiments,
results, and conclusions
— Submit the project code, the readme document, and some
testing samples (images, videos, etc.) for validation
• Presentation
6. Journals & Conferences
in Image Processing
• Journals:
— IEEE T IMAGE PROCESSING
— IEEE T MEDICAL IMAGING
— INTL J COMP. VISION
— IEEE T PATTERN ANALYSIS MACHINE INTELLIGENCE
— PATTERN RECOGNITION
— COMP. VISION AND IMAGE UNDERSTANDING
— IMAGE AND VISION COMPUTING
… …
• Conferences:
— CVPR: Comp. Vision and Pattern Recognition
— ICCV: Intl Conf on Computer Vision
— ACM Multimedia
— ICIP
— SPIE
— ECCV: European Conf on Computer Vision
— CAIP: Intl Conf on Comp. Analysis of Images and Patterns
… …
• IEEE Communications Surveys & Tutorials
• IEEE Multimedia
• IEEE Signal Processing Magazine
7. Text Books
• Rafael C. Gonzalez and Richard E. Woods, Digital Image
Processing, Prentice Hall, 2008, Third Edition.
• Rafael C. Gonzalez and Richard E. Woods, Digital Image
Processing Using Matlab, Prentice Hall, 2009, Second
Edition.
9. This lecture will cover
• Introduction
• State of the art examples of digital image processing
• Key stages in digital image processing
• Digital Image Processing Fundamentals (Part-1)
10. Introduction
• Interest comes from two primary backgrounds
Improvement of pictorial information for human perception
• How can an image/video be made more aesthetically pleasing
• How can an image/video be enhanced to facilitate extraction of
useful information
Processing of data for autonomous machine perception
11. • What is Digital Image Processing?
Digital Image
— a two-dimensional function
x and y are spatial coordinates
The amplitude of f is called intensity or gray level at the point (x, y)
Digital Image Processing
— process digital images by means of computer, it covers low-, mid-, and high-level
processes
low-level: inputs and outputs are images
mid-level: outputs are attributes extracted from input images
high-level: an ensemble of recognition of individual objects
Pixel
— the elements of a digital image
Introduction
12. What is DIP? (cont…)
•The continuum from image processing to computer vision
can be broken up into low-, mid- and high-level processes
Low Level Process
Input: Image
Output: Image
Examples: Noise
removal, image
sharpening
Mid Level Process
Input: Image
Output: Attributes
Examples: Object
recognition,
segmentation
High Level Process
Input: Attributes
Output: Understanding
Examples: Scene
understanding,
autonomous navigation
In this course we will
stop here
17. History of DIP
•1980s - Today: The use of digital image processing
techniques has exploded and they are now used for all
kinds of tasks in all kinds of areas
• Image enhancement/restoration
• Artistic effects
• Medical visualisation
• Industrial inspection
• Law enforcement
• Human computer interfaces
19. Examples: The Hubble Telescope
•Launched in 1990 the Hubble
telescope can take images of
very distant objects
•However, an incorrect mirror
made many of Hubble’s
images useless
•Image processing techniques
were used to fix this
20. Examples: Artistic Effects
•Artistic effects are used to
make images more
visually appealing, to add
special effects and to
make composite images
21. Examples: Medicine
•Take slice from MRI scan of canine heart, and find
boundaries between types of tissue
• Image with gray levels representing tissue density
• Use a suitable filter to highlight edges
Original MRI Image of a Dog Heart Edge Detection Image
22. Examples: GIS
•Geographic Information Systems
• Digital image processing techniques are used extensively to
manipulate satellite imagery
• Terrain classification
• Meteorology
23. Examples: GIS (cont…)
•Night-Time Lights of the
World data set
• Global inventory of human
settlement
• Not hard to imagine the kind of
analysis that might be done
using this data
24. Examples: Industrial Inspection
•Human operators are
expensive, slow and
unreliable
•Make machines do the
job instead
•Industrial vision systems
are used in all kinds of
industries
25. Examples: Law Enforcement
•Image processing techniques
are used extensively by law
enforcers
• Number plate recognition for speed
cameras/automated toll systems
• Fingerprint recognition
• Enhancement of CCTV images
26. Key Stages in Digital Image Processing
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
27. Key Stages in Digital Image Processing:
Image Aquisition
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
28. Key Stages in Digital Image Processing:
Image Enhancement
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
29. Key Stages in Digital Image Processing:
Image Restoration
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
30. Key Stages in Digital Image Processing:
Morphological Processing
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
31. Key Stages in Digital Image Processing:
Segmentation
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
32. Key Stages in Digital Image Processing:
Object Recognition
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
36. Computer Vision: Some Applications
• Optical character recognition (OCR)
• Face Detection
• Smile Detection
• Login without password using fingerprint scanners and
face recognition systems
• Object recognition in mobiles
• Smart Cars
• Vision in space
39. Electromagnetic (EM) energy spectrum
Major uses
Gamma-ray imaging: nuclear medicine and astronomical observations
X-rays: medical diagnostics, industry, and astronomy, etc.
Ultraviolet: lithography, industrial inspection, microscopy, lasers, biological imaging,
and astronomical observations
Visible and infrared bands: light microscopy, astronomy, remote sensing, industry,
and law enforcement
Microwave band: radar
Radio band: medicine (such as MRI) and astronomy
40. Light and EM Spectrum
c
, : Planck's constant.
E h h
41. Light and EM Spectrum
► The colors that humans perceive in an object
are determined by the nature of the light
reflected from the object.
e.g. green objects reflect light with wavelengths primarily in the 500
to 570 nm range while absorbing most of the energy at other
wavelength
42. Light and EM Spectrum
► Monochromatic light: void of color Intensity is the only attribute,
from black to white Monochromatic images are referred to as gray-
scale images
► Chromatic light bands: 0.43 to 0.79 um
The quality of a chromatic light source:
Radiance: total amount of energy
Luminance (lm): the amount of energy an observer perceives from a light
source
Brightness: a subjective descriptor of light perception that is impossible to
measure. It embodies the achromatic notion of intensity and one of the key
factors in describing color sensation.
47. A Simple Image Formation Model
( , ) ( , ) ( , )
( , ): intensity at the point ( , )
( , ): illumination at the point ( , )
(the amount of source illumination incident on the scene)
( , ): reflectance/transmissivity
f x y i x y r x y
f x y x y
i x y x y
r x y
at the point ( , )
(the amount of illumination reflected/transmitted by the object)
where 0 < ( , ) < and 0 < ( , ) < 1
x y
i x y r x y
48. Some Typical Ranges of illumination
• Illumination
Lumen — A unit of light flow or luminous flux
Lumen per square meter (lm/m2) — The metric unit of measure
for illuminance of a surface
• On a clear day, the sun may produce in excess of 90,000 lm/m2 of
illumination on the surface of the Earth
• On a cloudy day, the sun may produce less than 10,000 lm/m2 of
illumination on the surface of the Earth
• On a clear evening, the moon yields about 0.1 lm/m2 of illumination
• The typical illumination level in a commercial office is about 1000 lm/m2
49. Some Typical Ranges of Reflectance
• Typical values of reflectance r(x,y)
• 0.01 for black velvet
• 0.65 for stainless steel
• 0.80 for flat-white wall paint
• 0.90 for silver-plated metal
• 0.93 for snow
50. Image Sampling and Quantization
Digitizing the
coordinate
values
Digitizing the
amplitude
values
53. Representing Digital Images
• The representation of an M×N numerical array as
(0,0) (0,1) ... (0, 1)
(1,0) (1,1) ... (1, 1)
( , )
... ... ... ...
( 1,0) ( 1,1) ... ( 1, 1)
f f f N
f f f N
f x y
f M f M f M N
54. Representing Digital Images
• The representation of an M×N numerical array as
0,0 0,1 0, 1
1,0 1,1 1, 1
1,0 1,1 1, 1
...
...
... ... ... ...
...
N
N
M M M N
a a a
a a a
A
a a a
55. Representing Digital Images
• The representation of an M×N numerical array in
MATLAB
(1,1) (1,2) ... (1, )
(2,1) (2,2) ... (2, )
( , )
... ... ... ...
( ,1) ( ,2) ... ( , )
f f f N
f f f N
f x y
f M f M f M N
56. Representing Digital Images
• Discrete intensity interval [0, L-1], L=2k
• The number b of bits required to store a M × N
digitized image
b = M × N × k
58. Spatial and Intensity Resolution
• Spatial resolution
— A measure of the smallest discernible detail in an
image
— stated with line pairs per unit distance, dots (pixels)
per unit distance, dots per inch (dpi)
• Intensity resolution
— The smallest discernible change in intensity level
— stated with 8 bits, 12 bits, 16 bits, etc.
62. Image Interpolation
• Interpolation — Process of using known data to
estimate unknown values
e.g., zooming, shrinking, rotating, and geometric correction
• Interpolation (sometimes called resampling) — an
imaging method to increase (or decrease) the number of
pixels in a digital image.
Some digital cameras use interpolation to produce a larger image than
the sensor captured or to create digital zoom
64. Image Interpolation:
Bilinear Interpolation
2 ( , )
(1 ) (1 ) ( , ) (1 ) ( 1, )
(1 ) ( , 1) ( 1, 1)
( ), ( ), , .
f x y
a b f l k a b f l k
a b f l k a b f l k
l floor x k floor y a x l b y k
(x,y)
65. Image Interpolation:
Bicubic Interpolation
3 3
3
0 0
( , ) i j
ij
i j
f x y a x y
• The intensity value assigned to point (x,y) is obtained by the
following equation
• The sixteen coefficients are determined by using the sixteen
nearest neighbors.