ECE 468 / CS 519

Digital Image Processing

Introduction

Prof. Sinisa Todorovic

sinisa@eecs.oregonstate.edu

1

ECE 468: Digital Image Processing

•

Instructor:

Sinisa Todorovic

sinisa@eecs.oregonstate.edu

•

Ofﬁce:

2107 Kelley Engineering Center

•

Ofﬁce Hours:

Mon 4-5pm, or by appointment

•

Classes:

MWF 3-3:50pm, KEC 1003

•

Class website:

http://web.engr.oregonstate.edu/~sinisa/courses/OSU/ECE468/ECE468.html

2

Recommended Textbook

•

“Digital Image Processing”

by R.C. Gonzalez and R.E. Woods,

3rd edition

, Pearson Prentice Hall, 2008

•

Additional readings on the class website

3

Suggested Readings

•

“Digital Image Processing Using MATLAB,”

by R.C. Gonzalez,

R.E. Woods, and S. Eddins,

2nd edition

, Pearson Prentice

Hall, 2008

4

Course Objectives

•

Cover

basic

theory and algorithms

widely

used in image processing

•

Develop hands-on experience in processing images

•

Familiarize with MATLAB Image Processing Toolbox

•

Develop critical thinking about the state of the art

5

Prerequisites

6

Prerequisites

•

Signals and systems: ECE 351 and ECE 352

6

Prerequisites

•

Signals and systems: ECE 351 and ECE 352

•

Linear algebra

•

Matrices, Matrix Operations

•

Determinants, Systems of Linear Equations

•

Eigenvalues, Eigenvectors

6

Prerequisites

•

Signals and systems: ECE 351 and ECE 352

•

Linear algebra

•

Matrices, Matrix Operations

•

Determinants, Systems of Linear Equations

•

Eigenvalues, Eigenvectors

•

Statistics and probability

•

Probability density function, Probability distribution

•

Mean, variance, co-variance, correlation

•

Priors, Posteriors, Likelihoods

•

Gaussian distribution

6

Prerequisites

•

Signals and systems: ECE 351 and ECE 352

•

Linear algebra

•

Matrices, Matrix Operations

•

Determinants, Systems of Linear Equations

•

Eigenvalues, Eigenvectors

•

Statistics and probability

•

Probability density function, Probability distribution

•

Mean, variance, co-variance, correlation

•

Priors, Posteriors, Likelihoods

•

Gaussian distribution

•

Good programming skills

6

Requirements

7

Requirements

•

Homework

•

Turn-in a hard copy

•

Homework = Problem solving or Mini-project

•

Mini-project

must

be implemented in MATLAB

•

Homework

should

be an individual effort

•

Late homework will not be accepted

without prior approval

7

Requirements

•

Homework

•

Turn-in a hard copy

•

Homework = Problem solving or Mini-project

•

Mini-project

must

be implemented in MATLAB

•

Homework

should

be an individual effort

•

Late homework will not be accepted

without prior approval

•

Graduate students will be given approximately 20% larger

amount of work for homework assignments

7

Requirements

8

Requirements

•

Exam 1 on

November 4, 3-3:50pm, KEC 1003

8

Requirements

•

Exam 1 on

November 4, 3-3:50pm, KEC 1003

•

Exam 2 on

December 2, 3-3:50pm, KEC 1003

8

Grading Policy

•

Homework = 30%

•

Exam 1 = 35%

•

Exam 2 = 35%

•

Bonus: Participation in class

9

Academic Honesty -- Examples of Cheating

•

Bringing forbidden material or devices to the examination

•

Working on the exam before or after the ofﬁcial time allowed

•

Requesting a re-grade of work altered after the initial grading

•

Submitting a homework that is not your own work

10

What is a Digital Image?

11

What is a Digital Image?

•

Two-dimensional function

f(x,y)

or matrix

•

x, y,

f(x,y)

are discrete and ﬁnite

•

Image size =

max

x

x

max

y

-- e.g. 640x480

•

Pixel intensity value

f(x,y)

∈

[0, 255]

y

x

column

row

pixel

12

Pixel Values

Source: DIP/3e

13

Images are not Collections of Random Pixels

14

A Typical Digital Image Processing System

3D world

camera

algorithms

representations

users

problem understanding

trade offs

training data

expert systems

knowledge base

processed

image

input

image

15

Sources of Energy for Image Formation

Source: DIP/3e

16

Some Applications -- Medical Diagnostics

Gamma-ray imaging

Source: DIP/3e

X-ray imaging

17

Some Applications -- Magnetic Resonance Imaging

18

Some Applications -- Microscopy

Visible-light microscopy imaging

Source: DIP/3e

19

Some Applications -- Industrial Inspection

20

Some Applications -- Remote Sensing

Aerial images

Satellite images

21

Some Applications -- Infrared Satellite Images

Source: DIP/3e

22

Some Applications -- Storing Images

Blue-ray

DVD

Standard

DVD

23

Some Applications -- Transmitting Images

Video conferencing

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Some Applications -- Image Forensics

25

Fundamental Steps in Digital Image Processing

26

Fundamental Steps in Digital Image Processing

•

Acquisition

•

Spatial and frequency transforms

•

Enhancement (subjective)

•

Restoration (objective)

•

Color processing

•

Multi-resolution processing

•

Compression

•

Morphological processing

•

Segmentation

27

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