Course Name: DIGITAL IMAGE PROCESSING                

Degree: Bachelor

Code

Semester

Local Credits

ECTS Credits

Course Implementation, Hours/Week

Theoretical

Tutorial

Laboratory

EE457

Spring

3

4

3

0

0

Department

Electrical and Electronics Engineering

Course Type

Elective

Course Language

English

Instructor

Assoc. Prof. Dr. Yıldıray Yalman

Contact Information

D1-110

yyalman@pirireis.edu.tr

Office Hours

Monday (09:00-10:00)

Course Prerequisites

-

Course Category

by Content, %

Basic Sciences

Engineering Science

Engineering Design

General Education

15

60

20

5

Course Description

This course covers the topics of introduction to         digital image processing (DIP) principles, tools, techniques, and algorithms. Includes topics in image representation, analysis, filtering, and segmentation, and pattern recognition.  It also includes teaching an image processing software (MATLAB) tools for some assignments.

Course Objectives

The objective of the course is to teach the digital image processing methods. The main topics planned to be taught are listed below:

• The fundamentals of digital image processing
• Image transform used in digital image processing
• Image enhancement techniques used in digital image processing
• Image restoration techniques and methods used in digital image processing
• Image compression and segmentation used in digital image processing

Course Learning Outcomes

At the end of this course, the student should be able to:

1. Explain the main challenges behind the design of machine vision systems.

2. Describe the general processes of          image acquisition, storage, enhancement, segmentation, representation, and description.

3. Implement basic operations, filtering, compression and enhancement algorithms for monochrome as well as color images using MATLAB.

Instruction Methods and Techniques

Recitation by the use of power point presentations, problem solving exercises,

DIP platform(s), and homework.

Tutorial Place

Regular class rooms for recitation and example problems

Textbook

·R.C. Gonzalez, R.E. Woods, S.L. Eddins, “Digital Image Processing Using Matlab”, Prentice Hall, 978-0130085191.

Other References

·R.C. Gonzalez, R.E. Woods, “Digital Image Processing”, Prentice Hall, 978-0131687288.

·Al Bovik, The Essential Guide to Image Processing, Elsevier, 2^nd Edition, 978-0-12-374457-9.

·A. Murat Tekalp, “Digital Video Processing”, Prentice Hall, 978-0131900752.

·Digital Signal Processing: Principles, Algorithms, and Applications, J.G. Proakis, D. G. Manolakis, Prentice Hall, 978-0133737622.

·Digital Signal Processing: A Computer-Based Approach, S. Mitra, McGraw-Hill, 978-0077366766.

Homework & Projects

Students will be required to solve selected problems at chapter ends of the textbook so that they are prepared for the exams.

Laboratory Work

-

Computer Use

Power-point and problem solving.

Other Activities

-

Assessment Criteria

Activities

Quantity

Effects on Grading, %

Midterm Exam

1

30

Quizzes

5

15

Homework

5

15

Projects

Term Paper/Project

Laboratory Work

Other Activities

Final Exam

1

40

ECTS/

WORKLOAD TABLE

Activities

Count

Hours

Total

Workload

Lecture

14

3

42

Midterm

1

25

25

Quiz

5

4

20

Homework

5

2

10

Term Paper/Project

Laboratory Work

Practices

Tutorial

Seminar

Presentation

Field Study

Final Exam

1

30

30

Total Workload

127

Total Workload/25

127/25

Course ECTS Credits

5

Weeks

Topics

Course Outcomes

1

Digital image fundamentals

1

2

Matlab-Image Processing toolbox and basic applications

1

3

Basic concepts of the image processing: digital image, digital/analog video, pixel, resolution, bit depth, color concepts and formats.

1

4

Image files; (raw, yuv, ​​tiff, bmp, jpeg). Basic image operations: rotation, mirroring, translation, change in size (zoom).

2-3

5

Image enhancement; brightness and contrast settings: Thresholding, negation, histogram, contrast stretching.

2-3

6

Pixel Neighborhood operations; convolution, low-pass, high-pass filter, median (median) filter, edge detection, correlation.

2-3

7

Color spaces: RGB, HSI, YUV, CMYK, etc.

2-3

8

Midterm

9

Frequency domain, filtering, phase correlation

2-3

10

Morphological operations: spreading, erosion, opening, closing

2-3

11

Lossy and Lossless compression, JPEG

2-3

12

Image segmentation

2-3

13

Representation and Description

2-3

14

Object recognition

2-3

Program Outcomes

Level of Contribution*

N

P

C

a

An ability to apply knowledge of mathematics, science, and engineering

X

b

An  ability to design and conduct experiments, as well as to analyze and interpret data

X

c

An ability to design a system, component or process to meet desired needs

X

d

Ability to function on multi-disciplinary teams

X

e

An ability to identify, formulate, and solve engineering problems

X

f

An understanding of professional and ethical responsibility

X

g

An ability to communicate effectively

X

h

The broad education necessary to understand the impact of engineering solutions in a global and societal context

X

i

A recognition of the need for, and an ability to engage in life-long learning

X

j

A knowledge of contemporary issues

X

k

An ability to use the techniques, skills and modern engineering tools necessary for engineering practice

X

l

An ability to apply basic knowledge in communication, control, power electronics and computer tracks in the context of Electrical and Electronics Engineering

X

Course Outcomes

 Program Outcomes

I

II

III

a

X

b

X

c

X

d

X

e

X

f

X

g

X

h

X

i

X

j

X

X

k

X

X

l

X

Prepared by

Yıldıray Yalman

Date

January, 2018

 Signature