FACULTY OF ENGINEERING

Department of Software Engineering

CE 490 | Course Introduction and Application Information

Course Name
Introduction to Digital Image Processing
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
CE 490
Fall/Spring
3
0
3
5

Prerequisites
  To be a junior (3th year) student
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Problem Solving
Simulation
Application: Experiment / Laboratory / Workshop
Lecture / Presentation
Course Coordinator
Course Lecturer(s)
Assistant(s) -
Course Objectives This course introduces the fundamental principles and algorithms of digital image processing systems. The course covers image sampling and quantization; spatial and frequency domain image enhancement techniques; signal processing theories used for digital image processing, such as one- and two-dimensional convolution, and two-dimensional Fourier transformation; morphological image processing; color models and basic color image processing.
Learning Outcomes The students who succeeded in this course;
  • Apply techniques of smoothing, sharpening, histogram processing and filtering to process digital images,
  • Explain sampling and quantization for obtaining digital images from continuously sensed data,
  • Apply filtering techniques in the spatial domain to enhance digital images,
  • Apply filtering techniques in the frequency domain to enhance digital images,
  • Apply filtering techniques to restore images in the presence of noise only,
  • Describe commonly applied color models and their use in basic color image processing,
  • Use MATLAB image processing toolbox.
Course Description The following topics are included: Digital images as two-dimensional signals; two-dimensional convolution, Fourier transform, and discrete cosine transform; Image processing basics; Image enhancement; Image restoration; Image coding and compression.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
X
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Introduction Chapter 1. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
2 Digital image fundamentals Chapter 2. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
3 Histogram processing Chapter 3. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
4 Point processing, basic intensity transformations Chapter 3. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
5 Spatial filtering, convolution, smoothing filters Chapter 3. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
6 Spatial filtering, convolution, sharpening filters, combining spatial filtering techniques Chapter 3. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
7 Midterm Exam I
8 Filtering in the frequency domain, convolution theorem Chapter 4. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
9 Image restoration for noise removal Chapter 5. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
10 Morphological image processing Chapter 9. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
11 Midterm Exam II
12 Color image processing Chapter 6. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
13 Fundamentals of image compression Chapter 8. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
14 JPEG image compression algorithm Chapter 8. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
15 Semester Review
16 Final Exam

 

Course Notes/Textbooks

R. C. Gonzalez, R. E. Woods, “Digital Image Processing”, Prentice Hall, 3rd Ed., 2008, ISBN 013168728X.

Suggested Readings/Materials

R. C. Gonzalez, R. E. Woods, S. L. Eddins, “Digital Image Processing Using MATLAB”, Prentice Hall, 2nd Ed., 2009, ISBN 9780982085400.

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
-
-
Laboratory / Application
-
-
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
2
60
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
2
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
16
3
48
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
0
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
2
15
30
Final Exam
1
24
24
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have adequate knowledge in Mathematics, Science, Computer Science and Software Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems.

X
2

To be able to identify, define, formulate, and solve complex Software Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.

X
3

To be able to design, implement, verify, validate, document, measure and maintain a complex software system, process, or product under realistic constraints and conditions, in such a way as to meet the requirements; ability to apply modern methods for this purpose.

4

To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in software engineering applications; to be able to use information technologies effectively.

X
5

To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex Software Engineering problems.

6

To be able to work effectively in Software Engineering disciplinary and multi-disciplinary teams; to be able to work individually.

7

To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to be able to present effectively, to be able to give and receive clear and comprehensible instructions.

8

To have knowledge about global and social impact of engineering practices and software applications on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Engineering and Software Engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications.

10

To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.

11

To be able to collect data in the area of Software Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1)

12

To be able to speak a second foreign language at a medium level of fluency efficiently.

13

To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Software Engineering.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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