PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
EEEN 444	DIGITAL IMAGE PROCESSING	3 + 0	7th Semester	4

COURSE DESCRIPTION

Course Level	Bachelor's Degree
Course Type	Elective
Course Objective	To teach the high-level image processing fundamentals and in this manner, to provide students with simulating digital image processing algorithms through extensive use of Matlab.
Course Content	Morphological image processing / Image feature extraction / Image segmentation / Image representation and description / Image classification.
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	He/She knows the morphological image processing methods
2	He/She knows edge detection algorithms and feature extraction
3	He/She knows and applies image segmentation.
4	He/She knows and applies image classification.

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07	PO 08	PO 09	PO 10	PO 11
LO 001	1	5	3	3	4	3	2	1		3	2
LO 002	4	5	2	2	2	1	1	1	2		2
LO 003	4	5	2	1	1	1	1	1		3	3
LO 004		5	2	2	2	2	1	1	2		2
Sub Total	9	20	9	8	9	7	5	4	4	6	9
Contribution	2	5	2	2	2	2	1	1	1	2	2

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	3	42
Mid-terms	1	6	6
Final examination	1	14	14
Total Work Load ECTS Credit of the Course			104 4

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2023-2024 Fall	2	AYDIN KIZILKAYA

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
EEEN 444	DIGITAL IMAGE PROCESSING	3 + 0	2	Turkish	2023-2024 Fall

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Prof. Dr. AYDIN KIZILKAYA	akizilkaya@pau.edu.tr		MUH A0313	%

Goals		To teach the high-level image processing fundamentals and in this manner, to provide students with simulating digital image processing algorithms through extensive use of Matlab.
Content		Morphological image processing / Image feature extraction / Image segmentation / Image representation and description / Image classification.

Topics

Weeks	Topics
1	Introduction
2	Color Space, grayscale and colorful images, image types
3	Fundamentals of Image Processing
4	Fourier Transformations
5	Image Filtering, Image Histogram
6	Edge Detection Methods
7	Image Segmentation - I
8	Image Segmentation - II
9	Image Enhancement
10	Midterm Exam
11	Image Restoration
12	Image Registration - I
13	Image Registration - II
14	Final Exam

Materials

Materials are not specified.

Resources

Resources	Resources Language
Internette ve kütüphanemizdeki konularla ilgili kaynak kitaplar, derste sunulan notlar	Türkçe

Course Assessment

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student