ECU 3040 Digital Image Processing - National Institute of ... · ECU 3040 Digital Image Processing...
-
Upload
nguyenduong -
Category
Documents
-
view
238 -
download
1
Transcript of ECU 3040 Digital Image Processing - National Institute of ... · ECU 3040 Digital Image Processing...
PreliminariesSummary
ECU 3040 Digital Image Processing
Dr. Praveen Sankaran
Department of ECE
NIT Calicut
January 8, 2015
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Ground Rules
Grading Policy:
Projects 20
Exam 1 15
Exam 2 15
Exam 3 50
Letter Grading:Absolute
Textbook:
Gonzalez and Woods, �Digital Image Processing 3rd Ed.�, Prentice
Hall, 2007.
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Outcomes
1 Ability to apply the knowledge of imaging systems to
implement real world systems.
2 Design image enhancement algorithms and implement systems
that utilize your algorithms.
3 Ability to work with and develop open source resources to
solve image processing problems.
4 Ability to test and verify (analyze) the soudness of various
algorithms.
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Topics
Imaging systems, quantization.
Histograms and histogram based modi�cations, spatial
�ltering, nonlinear spatial image enhancement.
Frequency domain, homomorphic �ltering, Retinex.
Morphological image processing, segmentation.
Denosizing, haze, blur removal.
HDR imaging, tone mapping.
Image quality assesment.
Imaging for security.
Patterns, classes, decision theory, networks.
OpenCV - applications, live projects - end sem.
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Plagiarism policy
Homework assignments and design projects are to be the work of
an individual student only. Evidence of foul play, if detected will
result in appropriate action against all concerned. Students may
discuss among themselves, but the �nal work need to be their own.
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Outline
1 Preliminaries
Image and image creation
Image sensing and acquisition
2 Summary
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
Outline
1 Preliminaries
Image and image creation
Image sensing and acquisition
2 Summary
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
Image
Two-dimensional (2-D) (discrete) representation of a
(continuous) physical three-dimensional (3-D) scene.
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
Image Formation
By the wavelength (or frequency) of the emitted or re�ected
radiation. Examples: Visible, Infrared, TeraHertz.
By the modality with which the image is acquired:
Passive:
visible
passive infrared
Active
acoustic or ultrasound
X-ray
TeraHertz.
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
EM Spectrum
Figure : The Electromagnetic (EM) Spectrum�reproduced from theLawrence Berkeley Labs website.
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
Visible Spectrum
Figure : Narrow visible spectrum
Energy
E = hν
h - Planck's constant, ν - frequncy
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
Visible Spectrum
400 - 700 nanometers - wavelength
(Some special people able to go from 380 - 780 nanometers)
450 - 750 terrahertz
Maximum sensitivity of eye - 555 nanometers - green region.
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
Image Formation - further divides
By the image capture device.
Examples: CCD or CMOS (visible)uncooled mi- crobolometer (Infrared)ultrasound transducer
By the coordinate system of the displayed image:
rectangular Cartesian coordinate system for most modalities.ultrasound and radar which are both polar.
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
Outline
1 Preliminaries
Image and image creation
Image sensing and acquisition
2 Summary
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
Case of a visible image
photo-detector
array.
continuous
amplitude,
continuous extent
radiance.
to continuous
amplitude but
discrete point
de�ned.Figure : Single imaging sensor
Amplitude dependence
Amplitude of the signal ∝ strength of the radiance �eld at that
point.
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
Approach to Imaging
Spatial sampling: de�nes the continuous radiance �eld only at
discrete locations;
Brightness quantization: converts the continuous amplitude to
a discrete set of values.
Achromatic image: shades of gray from black to white
f (x ,y) = image brightness at spatial location x ,y .(real-valued, non-negative, and bounded ).
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
Approach to Imaging
Spatial sampling: de�nes the continuous radiance �eld only at
discrete locations;
Brightness quantization: converts the continuous amplitude to
a discrete set of values.
Achromatic image: shades of gray from black to white
f (x ,y) = image brightness at spatial location x ,y .(real-valued, non-negative, and bounded ).
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
Approach to Imaging
Spatial sampling: de�nes the continuous radiance �eld only at
discrete locations;
Brightness quantization: converts the continuous amplitude to
a discrete set of values.
Achromatic image: shades of gray from black to white
f (x ,y) = image brightness at spatial location x ,y .(real-valued, non-negative, and bounded ).
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
Figure : Image brightnessfunction Figure : Rectangular sampling
grid
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
Point Spread Function
Spatial sampling associates with each pixel [m,n] an �average�
brightness f̄ [m,n] that is determined primarily by the
brightness of the points within the pixel.
The actual brightness
contribution to f̄ [m,n]from points within the
pixel and from
neighboring points outside
the pixel is determined by
a point spread or
�weighting� function h.f̄ [m,n] =∫x
∫y f (x ,y)h (m,n;x ,y)dxdy
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
Brightness Quantization
Analog →Digital process.
Associate a non-negative integer value, l , with each of the real
valued values of f̄ [m,n].
Gray level, l
l = 0,1 · · ·L−1, where L = 2b
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Basic ProblemImage sensing and acquisition
Uniform Brightness Quantization
This is the most popular.
Decision levels
Ql+1−Ql = β−α
L
Values of f̄ [m,n] less than α or greater than β are clipped to
0 or L−1respectively.
Total number of bits required to represent the image:
b×M×N.
Dr. Praveen Sankaran DIP Winter 2014-15
PreliminariesSummary
Summary
A pixel is a small image area indexed by [m,n];
g [m,n] is the associated pixel value;
the possible values of g [m,n] are the gray levels
l = 0,1 · · ·L−1;
a digital image is an M×N array of gray levels.
Dr. Praveen Sankaran DIP Winter 2014-15
Acknowledgement
The material taught in this class is heavily in�uenced by work of
Dr. Zia Rahman.
Zia-ur Rahman joined the Electrical and Computer Engineering
Department at Old Dominion University, as an Associate Professor
in 2006. Before that he was a Research Associate Professor with
the Department of Applied Science at the College of William &
Mary. He received a B.A. in Physics from Ripon College in 1984,
and an M.S. and a Ph.D. in Electrical Engineering from the
University of Virginia in 1986 and 1989, respectively. His graduate
research focused on using neural networks and image processing
Dr. Praveen Sankaran DIP Winter 2014-15