ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH, PHD)
PhD	TR-NQF-HE: Level 8	QF-EHEA: Third Cycle	EQF-LLL: Level 8

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
CMP5126	Image and Video Processing	Fall	3	0	3	12

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :	Assist. Prof. LAVDİE RADA ÜLGEN
Recommended Optional Program Components:	None
Course Objectives:	Digital image processing consists of understanding and gaining knowledge about the fundamentals of digital image processing, image transformation, image enhancement techniques, image restoration techniques, image compression, and segmentation. Students should obtain an intuitive understanding of the implementation of basic sciences and mathematics in solving image processing problems. Students should be able to produce skillful analyses, and design and develop a system/component/ process for a required needs analysis and interpretation of a given digital image data. Students should gain some experience in the implementation of image processing methods by using MATLAB. The course trains the students to approach multidisciplinary engineering challenges involving image processing and prepares them to compete for a successful career in the engineering profession through global education standards.

Learning Outcomes

The students who have succeeded in this course;
The students who have succeeded in this course will be able to:
1. learn and understand the image representation and imaging systems;
2. fundamentals of digital image processing and image representation;
3. learn and understand image enhancement techniques;
4. learn and understand image restoration techniques and methods;
5. learn and understand morphological image processing manipulation;
6. learn and understand image segmentation and registration;
7. learn and understand image compression;
8. code in Matlab real-life-related problems.

Course Content

This course is essential for engineering programs as it equips students with practical skills in image analysis, mathematical modeling, and computational techniques. Students learn to solve complex systems, perform error analysis, and apply interpolation, curve fitting, and least-squares methods, which are vital for data analysis and optimization in engineering. The course covers numerical differentiation, integration, and Fourier transformations, essential for fields like signal processing and fluid mechanics. Additionally, students gain experience in eigenvalue problems and singular value decomposition, relevant for control systems and image processing. Through MATLAB, students implement and test numerical methods, enhancing their problem-solving abilities in real-world engineering applications.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to image processing
2)	Image representation and imaging systems
3)	Fundamentals of image processing
4)	Image Enhancement - Histogram Modeling
5)	Image enhancement in the frequency domain
6)	Model of Image Degradation/ Restoration process
7)	Midterm
8)	Morphological image F2F processing
9)	Blurring and Deblurring
10)	Image Segmentation: Detection of discontinuities
11)	Color image processing
12)	Different application of image processing
13)	Basics of analog and digital video
14)	Revision and project presentation

Sources

Course Notes / Textbooks:	1. Rafael C Gonzalez, Richard E Woods, "Digital Image Processing" - 2nd Edition, Pearson Education 2003 2. Geoff Dougherty 'Digital Image Processing for Medical Applications' Cambridge University Press, 2009
References:	1. T2. Jain A.K., "Fundamentals of Digital Image Processing", Pearson education 2. William K Pratt, "Digital Image Processing", John Willey 2001 3. Millman Sonka, Vaclav Hlavac, Roger Boyle, Broos/Colic, "Image Processing Analysis and Machine Vision" - Thompson Learning, 1999. 4. Chanda S., Dutta Majumdar - "Digital Image Processing and Applications", Prentice Hall of India, 2000

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Quizzes	2	% 10
Homework Assignments	1	% 15
Project	1	% 35
Midterms	1	% 10
Final	1	% 30
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 35
PERCENTAGE OF FINAL WORK		% 65
Total		% 100

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Have sufficient background and an ability to apply knowledge of mathematics, science, and engineering to identify, formulate, and solve problems of electrical and electronics engineering
2)	Be able to define, formulate and solve sophisticated engineering problems by choosing and applying appropriate analysis and modeling techniques and using technical symbols and drawings of electrical and electronics engineering for design, application and communication effectively.
3)	Have an ability to design or implement an existing design of a system, component, or process to meet desired needs within realistic constraints (realistic constraints may include economic, environmental, social, political, health and safety, manufacturability, and sustainability issues depending on the nature of the specific design)
4)	Be able to develop, choose, adapt and use innovative and up-to-date techniques, skills, information technologies, and modern engineering tools necessary for electrical and electronics engineering practice and adaptation to new applications.
5)	Be able to design and conduct experiments, as well as to collect, analyze, and interpret relevant data, and use this information to improve designs.
6)	Be able to function individually as well as to collaborate with others in multidisciplinary teams.
7)	Be able to communicate effectively in English and Turkish (if he/she is a Turkish citizen).
8)	Be able to recognize the need for, and to engage in life-long learning as well as a capacity to adapt to changes in the technological environment.
9)	Have a consciousness of professional and ethical responsibilities as well as workers’ health, environment and work safety.
10)	Have the knowledge of business practices such as project, risk, management and an awareness of entrepreneurship, innovativeness, and sustainable development.
11)	Have the broad knowledge necessary to understand the impact of electrical and electronics engineering solutions in a global, economic, environmental, legal, and societal context.