ECU 3040 Digital Image Processing - National Institute of ... · ECU 3040 Digital Image Processing ... Gonzalez and Woods, Digital Image Processing 3rd ... neural networks and image

PreliminariesSummary

ECU 3040 Digital Image Processing

Dr. Praveen Sankaran

Department of ECE

NIT Calicut

January 8, 2015

Dr. Praveen Sankaran DIP Winter 2014-15


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.



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.



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.



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.



Outline

1 Preliminaries

Image and image creation

Image sensing and acquisition

2 Summary



Basic ProblemImage sensing and acquisition

Outline

1 Preliminaries



2 Summary




Image

Two-dimensional (2-D) (discrete) representation of a

(continuous) physical three-dimensional (3-D) scene.




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.




EM Spectrum

Figure : The Electromagnetic (EM) Spectrum�reproduced from theLawrence Berkeley Labs website.




Visible Spectrum

Figure : Narrow visible spectrum

Energy

E = hν

h - Planck's constant, ν - frequncy




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.




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.




Outline

1 Preliminaries



2 Summary




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.




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 ).




Approach to Imaging


discrete locations;








Approach to Imaging


discrete locations;








Figure : Image brightnessfunction Figure : Rectangular sampling

grid




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




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




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.



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.


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


Acknowledgement

techniques for motion detection and target tracking.


ECU 3040 Digital Image Processing - National Institute of ... · ECU 3040 Digital Image Processing ... Gonzalez and Woods, Digital Image Processing 3rd ... neural networks and image

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