Department of Electrical and Electronics Engineering

ACADEMICS
Course Details

ELE490 - Fundamentals of Image Processing

2024-2025 Fall term information
The course is open this term
Supervisor(s)
Name Surname Position Section
Dr. Onur Külçe Supervisor 21
Weekly Schedule by Sections
Section Day, Hours, Place
21 Wednesday, 13:40 - 16:30, E6

Timing data are obtained using weekly schedule program tables. To make sure whether the course is cancelled or time-shifted for a specific week one should consult the supervisor and/or follow the announcements.

ELE490 - Fundamentals of Image Processing
Program Theoretıcal hours Practical hours Local credit ECTS credit
Undergraduate 3 2 4 7
Obligation : Elective
Prerequisite courses : ELE301
Concurrent courses : -
Delivery modes : Face-to-Face
Learning and teaching strategies : Lecture, Question and Answer, Problem Solving, Programming
Course objective : This course provides an introduction to the theory and applications of image processing using Python as the programming language. Topics include image formation, image transforms, image filtering, image enhancement, image restoration, segmentation, compression and color image processing. Concepts of pattern recognition such as object recognition, deep learning and clustering will be studied as applied to image processing.
Learning outcomes : A student who completes the course successfully is knowledgeable about image acquisition and formation, is able to filter an image in both the spatial and the frequency domains; restore, segment, and compress images using a programming language; and perform image classification and object recognition using deep learning. The student will have both theoretical and practical knowledge of image processing and apply his/her knowledge to real-world problems.
Course content : Python as a programming language, Image Acquisition and Digital Image Formation, Intensity Transformations, Spatial Filtering, Filtering in the Frequency Domain, Image Restoration and Reconstruction, Color Image Processing, Image Compression, Morphological Image Processing, Image Segmentation, Deep Learning, Image Classification and Object Recognition using Deep Learning.
References : Rafael C. Gonzalez & Richard E. Woods, Digital Image Processing, Addison Wesley. Practical Machine Learning and Image Processing, Himanshu Singh, Apres, 2019.
Course Outline Weekly
Weeks Topics
1 Introduction to Image Processing, Introduction to Python Programming: Fundamentals
2 Introduction to Python Programming: Data Science Tools
3 Image Acquisition and Digital Image Formation
4 Intensity Transformations
5 Spatial Filtering
6 Filtering in the Frequency Domain
7 Image Restoration
8 Midterm
9 Color Image Processing
10 Image Compression
11 Morphological Image Processing
12 Image Segmentation
13 Fundamentals of Deep Learning
14 Image Classification and Object Detection using Deep Learning
15 Final exam preparation
16 Final exam
Assessment Methods
Course activities Number Percentage
Attendance 0 0
Laboratory 0 0
Application 0 0
Field activities 0 0
Specific practical training 0 0
Assignments 4 20
Presentation 0 0
Project 1 10
Seminar 0 0
Quiz 0 0
Midterms 1 30
Final exam 1 40
Total 100
Percentage of semester activities contributing grade success 60
Percentage of final exam contributing grade success 40
Total 100
Workload and ECTS Calculation
Course activities Number Duration (hours) Total workload
Course Duration 14 3 42
Laboratory 0 0 0
Application 0 0 0
Specific practical training 0 0 0
Field activities 0 0 0
Study Hours Out of Class (Preliminary work, reinforcement, etc.) 14 5 70
Presentation / Seminar Preparation 0 0 0
Project 1 30 30
Homework assignment 4 3 12
Quiz 0 0 0
Midterms (Study Duration) 1 25 25
Final Exam (Study duration) 1 40 40
Total workload 35 106 219
Matrix Of The Course Learning Outcomes Versus Program Outcomes
Key learning outcomes Contribution level
1 2 3 4 5
1. Possesses the theoretical and practical knowledge required in Electrical and Electronics Engineering discipline.
2. Utilizes his/her theoretical and practical knowledge in the fields of mathematics, science and electrical and electronics engineering towards finding engineering solutions.
3. Determines and defines a problem in electrical and electronics engineering, then models and solves it by applying the appropriate analytical or numerical methods.
4. Designs a system under realistic constraints using modern methods and tools.
5. Designs and performs an experiment, analyzes and interprets the results.
6. Possesses the necessary qualifications to carry out interdisciplinary work either individually or as a team member.
7. Accesses information, performs literature search, uses databases and other knowledge sources, follows developments in science and technology.
8. Performs project planning and time management, plans his/her career development.
9. Possesses an advanced level of expertise in computer hardware and software, is proficient in using information and communication technologies.
10. Is competent in oral or written communication; has advanced command of English.
11. Has an awareness of his/her professional, ethical and social responsibilities.
12. Has an awareness of the universal impacts and social consequences of engineering solutions and applications; is well-informed about modern-day problems.
13. Is innovative and inquisitive; has a high level of professional self-esteem.
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest
General Information | Course & Exam Schedules | Real-time Course & Classroom Status
Undergraduate Curriculum | Minor Program For Non-departmental Students | Open Courses, Sections and Supervisors | Weekly Course Schedule | Examination Schedules | Information for Registration | Prerequisite and Concurrent Courses | Legal Info and Documents for Internship | Academic Advisors for Undergraduate Program | Information for ELE 401-402 Graduation Project | Virtual Exhibitions of Graduation Projects | Erasmus+ Program | Program Educational Objectives & Student Outcomes | ECTS Course Catalog | HU Registrar's Office
Graduate Curriculum | Open Courses and Supervisors | Weekly Course Schedule | Final Examinations Schedule | Schedule of Graduate Thesis Defences and Seminars | Information for Registration | ECTS Course Catalog - Master's Degree | ECTS Course Catalog - PhD Degree | HU Graduate School of Science and Engineering