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Course Information

Course Title	Code	Semester	Laboratory+Practice (Hour)	Pool	Type	ECTS
Dijital Image Processing	BİL438	FALL-SPRING	2+0		E	4

Learning Outcomes	1-Students will understand fundamental underlying principles of digital image processing. 2-Student will learn the basics theory of image processing(hardware and software, digitization, enhancement and restoration, encoding, segmentation, feature detection etc.) 3-Student will understand how to apply such knowledge on real examples of image processing tasks. 4-Student will analyze and implement image processing algorithms. 5-Student will complete a project, write report and present in class on a topic in image processing

ECTS / WORKLOAD

Activity	Percentage (100)	Number	Time (Hours)	Total Workload (hours)
Course Duration (Weeks x Course Hours)		14	2	28
Classroom study (Pre-study, practice)		14	3	42
Assignments	10	1	5	5
Short-Term Exams (exam + preparation)	0	0	0	0
Midterm exams (exam + preparation)	30	1	10	10
Project	0	0	0	0
Laboratory	0	0	0	0
Final exam (exam + preparation)	60	1	20	20
	0	0	0	0
Total Workload (hours)				105
Total Workload (hours) / 30 (s)				3,5 ---- (4)
ECTS Credit				4

Course Content

Week	Topics	Study Metarials
1	Introduction	R1-Chapter-1
2	Digital Image Fundamentals	R1-Chapter-2
3	Image Enhancement Techniques	R1-Chapter-3
4	Spatial Filtering	R1-Chapter-4
5	Color Image Processing	R1-Chapter-5
6	Image Segmentation I	R1-Chapter-6
7	Image Segmentation II	R1-Chapter-6
8	Morphological Image Processing	R1-Chapter-6
9	Image Representation and Description	R1-Chapter-7
10	Motion Anaysis	R1-Chapter-8
11	Pattern Recognition	R1-Chapter-9
12	Deep Learning for Image Processing Applications I	R1-Chapter-10
13	Deep Learning for Image Processing Applications II	R1-Chapter-10
14	Deep Learning for Image Processing Applications III	R1-Chapter-10

Prerequisites	-
Language of Instruction	Turkish
Responsible	Assist. Prof. Dr. Mustafa KARHAN
Instructors	-
Assistants	-
Resources	R1. Gonzale, R. C., & Woods R. E.(2008).Digital Image Processing, Prentice Hall, 3rd Edition.
Supplementary Book	-
Goals	The aim of this course is to teach students principle techniques and algorithms of image processing
Content	Digital Image Fundamentals, Image Enhancement Techniques, Spatial Filtering, Color Image Processing, Image Segmentation, Morphological Image Processing, Texture Analysis, Image Representation and Description, Image Compression, Motion Analysis, Pattern Recognition, Deep Learning for Image Processing Applications

Program Learning Outcomes

	Program Learning Outcomes	Level of Contribution
1	To be able to apply mathematics, science and engineering theories and principles to Computer Engineering problems.	5
2	To have the ability to define, model, and solve problems related to Computer Engineering.	3
3	To be able to design and conduct experiments, as well as to analyze and interpret data.	5
4	To be able to design and analyze a process for a specific purpose within technical and economical limitations.	-
5	To be able to use modern techniques and calculation tools required for engineering applications.	5
6	To have the awareness of professional liabilities and ethics.	-
7	To be able to get involved in interdisciplined and multidisciplined team work.	-
8	To be able to declare his/her opinions orally or written in a clear, concise and brief manner.	-
9	To improve him/herself by following the developments in science, technology, modern issues, and know the importance of lifelong learning.	-
10	To be able to evaluate engineering solutions for the global and social problems especially for the health, safety, and environmental problems.	-
11	To have knowledge about of contemporary issues.	-

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