M.SC. in Computer Engineering (Without Thesis)

EEE 501 | Course Introduction and Application Information

Course Name

Applied Digital Image Processing

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
EEE 501	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Second Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
National Occupation Classification	-
Course Coordinator	Doç. Dr. Mehmet Türkan
Course Lecturer(s)	Doç. Dr. Mehmet Türkan
Assistant(s)	-

Course Objectives

This course aims to teach the fundamental principles and techniques of digital image processing and their applications to engineering problems.The course covers topics such as point operations, image filtering and deconvolution, eigenimages, noise reduction and restoration, color image processing, multi-resolution processing, image compression, morphological image processing,scale-space techniques, feature extraction and recognition, image thresholding/segmentation, image registration and image matching, and software applications

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	know how to use the principles of image formation, sampling and quantization while analyzing image processing techniques and systems,
2	know how to use intensity transformations, spatial- and frequency-domain filtering techniques to process and enhance images,
3	know how to use specific techniques to restore images in the presence of different noise and degradation processes,
4	know how to apply the most common color models and use them in color image processing,
5	have knowledge of the advantages and limitations of different digital image processing algorithms,
6	be able to evaluate potential applications of digital image processing techniques,
7	be able to design and implement digital image processing algorithms using Matlab and image processing toolbox.

Course Description

Image filtering and deconvolution, eigenimages, noise reduction and restoration, color image processing, multi-resolution processing, image compression, morphological image processing,scale-space techniques, feature extraction and recognition, image thresholding/segmentation, image registration and image matching.

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Related Preparation	Learning Outcome
1	Introduction, Application Areas of Digital Image Processing	Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 1)
2	Digital Image Fundamentals. Sampling, Quantization, Aliasing, Moire patterns	Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 2)
3	Point Operations,Image Intensity Transformations, Histogram Processing	Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 3)
4	Spatial Filtering, Image Enhancement in Spatial Domain	Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 3)
5	Frequency Domain Filtering and Image Enhancement	Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 4)
6	Image Restoration and Reconstruction	Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 5)
7	Image Restoration and Reconstruction	Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 5)
8	Midterm
9	Color Image Processing, Color Transformations
10	Multiresolution Image Processing	Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 6)
11	Image Compression	Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch.7)
12	Morphological Image Processing	Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 9)
13	Image Segmentation and Registration	Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 10)
14	In-class Presentations
15	In-class Presentations
16	Review of the Semester

Course Notes/Textbooks	The textbook referenced above and course slides
Suggested Readings/Materials	Related Research Papers

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5	LO 6	LO 7
Participation	-	-
Laboratory / Application		-
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	1	30
Presentation / Jury
Project	1	40
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam
Total

Weighting of Semester Activities on the Final Grade	3	100
Weighting of End-of-Semester Activities on the Final Grade
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	1	20	20
Presentation / Jury			0
Project	1	89	89
Seminar / Workshop			0
Oral Exam			0
Midterms	1	20	20
Final Exam			0
		Total	225

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.		-	-	-	X	-
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	-	-	-	X
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	-	-	X	-
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	-	-	X
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	X
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	-	-	X
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		-	-	-	X	-
8	Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility.		-	-	-	-	X
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		-	-	X	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		-	-	X	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.		-	-	X	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		-	X	-	-	-