FACULTY OF ENGINEERING

Department of Industrial 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
Service Course
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
X
Supportive Courses
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 and Industrial Engineering; to be able to use theoretical and applied information in these areas to model and solve Industrial Engineering problems.

X
2

To be able to identify, formulate and solve complex Industrial Engineering problems by using state-of-the-art methods, techniques and equipment; to be able to select and apply proper analysis and modeling methods for this purpose.

X
3

To be able to analyze a complex system, process, device or product, and to design with realistic limitations to meet the requirements using modern design techniques.

X
4

To be able to choose and use the required modern techniques and tools for Industrial Engineering applications; to be able to use information technologies efficiently.

X
5

To be able to design and do simulation and/or experiment, collect and analyze data and interpret the results for investigating Industrial Engineering problems and Industrial Engineering related research areas.

X
6

To be able to work efficiently in Industrial Engineering disciplinary and multidisciplinary teams; to be able to work individually.

X
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 present effectively; to be able to give and receive clear and comprehensible instructions

8

To have knowledge about contemporary issues and the global and societal effects of Industrial Engineering practices on health, environment, and safety; to be aware of the legal consequences of Industrial Engineering solutions.

X
9

To be aware of professional and ethical responsibility; to have knowledge of the standards used in Industrial Engineering practice.

X
10

To have knowledge about business life practices such as project management, risk management, and change management; to be aware of entrepreneurship and innovation; to have knowledge about sustainable development.

X
11

To be able to collect data in the area of Industrial Engineering; to be able to communicate with colleagues in a foreign language.

X
12

To be able to speak a second foreign 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 Industrial Engineering.

X

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

 


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