SECTION I: GENERAL INFORMATION ABOUT THE COURSE |
Course Code | Course Name | Year | Semester | Theoretical | Practical | Credit | ECTS |
70610MEEOS-CME0690 | Image Processing | 0 | Fall |
3 | 0 | 3 | 6 |
Course Type : | Non-Departmental Elective |
Cycle: | Master TQF-HE:7. Master`s Degree QF-EHEA:Second Cycle EQF-LLL:7. Master`s Degree |
Language of Instruction: | English |
Prerequisities and Co-requisities: | N/A |
Mode of Delivery: | Face to face |
Name of Coordinator: | Profesör Dr. ABDURAZZAG ALI A ABURAS |
Dersin Öğretim Eleman(lar)ı: |
Öğretim Görevlisi Dr. ENVER AKBACAK Profesör Dr. ABDURAZZAG ALI A ABURAS |
Dersin Kategorisi: |
SECTION II: INTRODUCTION TO THE COURSE |
Course Objectives: | Course Learning Outcomes (CLOs) are those describing the knowledge, skills, and competencies that students are expected to achieve upon successful completion of the course. In this context, the course Learning Outcomes defined for this course unit are as follows: 1) Knowledge (Described as Theoretical and/or Factual Knowledge) 2) Skills (Describe as Cognitive and/or Practical Skills.) 3) Competences (Described as the "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field-specific" competencies.) |
Course Content: | 1) Introduction to Computer Vision. 2) Color Texture Image Basics 3) Image Coordinates and Resizing 4) Digital Filters, and Image Transformations 5) Edges and Features 6) Corner Detection-Edges and Features 7) Describing and Matching 8) Matching and Blending 9) Content-Based Image Retrieval (CBIR) and the EM Algorithm 10) Features and Flow 11) Basics for Convolutional Neural Networks (CNN) 12) Generative Adversarial Networks (GANs) |
Linear algebra, basic calculus, and probability Experience with image processing will help but is not necessary. Experience with Python or Python-like languages will help |
Knowledge (Described as Theoritical and/or Factual Knowledge.) | ||
Skills (Describe as Cognitive and/or Practical Skills.) | ||
1) Ability to solve a complex problem using advanced image processing techniques. |
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Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.) |
Course Notes / Textbooks: | all are available on OIS |
References: | 1) Recommended: Computer Vision: Algorithms and Applications Richard Szelisk, 2010 2) Hands-On Image Processing with Python: Expert techniques for advanced image analysis and effective interpretation of image data by Sandipan Dey 2025, ISBN: 978-1789343731 |
SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs) |
CLOs/PLOs | KPLO 1 | KPLO 2 | KPLO 3 | KPLO 4 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 1 | 1 | 2 | 3 | 4 | 5 | 1 | 2 | 3 | 4 | 5 | 6 | |
CLO1 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE |
Lectures | |
Discussion | |
Case Study | |
Problem Solving | |
Demonstration | |
Views | |
Laboratory | |
Reading | |
Homework | |
Project Preparation | |
Thesis Preparation | |
Peer Education | |
Seminar | |
Technical Visit | |
Course Conference | |
Brain Storming | |
Questions Answers | |
Individual and Group Work | |
Role Playing-Animation-Improvisation | |
Active Participation in Class |
Midterm | |
Presentation | |
Final Exam | |
Quiz | |
Report Evaluation | |
Homework Evaluation | |
Oral Exam | |
Thesis Defense | |
Jury Evaluation | |
Practice Exam | |
Evaluation of Implementation Training in the Workplace | |
Active Participation in Class | |
Participation in Discussions |
LEARNING & TEACHING METHODS | ASSESMENT & EVALUATION METHODS | ||||||||||||||||||||
CLO1 | |||||||||||||||||||||
-Lectures | -Midterm | ||||||||||||||||||||
-Discussion | -Presentation | ||||||||||||||||||||
-Case Study | -Final Exam | ||||||||||||||||||||
-Problem Solving | -Quiz | ||||||||||||||||||||
-Demonstration | -Report Evaluation | ||||||||||||||||||||
-Views | -Homework Evaluation | ||||||||||||||||||||
-Laboratory | -Oral Exam | ||||||||||||||||||||
-Reading | -Thesis Defense | ||||||||||||||||||||
-Homework | -Jury Evaluation | ||||||||||||||||||||
-Project Preparation | -Practice Exam | ||||||||||||||||||||
-Thesis Preparation | -Evaluation of Implementation Training in the Workplace | ||||||||||||||||||||
-Peer Education | -Active Participation in Class | ||||||||||||||||||||
-Seminar | - Participation in Discussions | ||||||||||||||||||||
-Technical Visit | |||||||||||||||||||||
-Course Conference | |||||||||||||||||||||
-Brain Storming | |||||||||||||||||||||
-Questions Answers | |||||||||||||||||||||
-Individual and Group Work | |||||||||||||||||||||
-Role Playing-Animation-Improvisation | |||||||||||||||||||||
-Active Participation in Class |
Measurement and Evaluation Methods | # of practice per semester | Level of Contribution |
Project | 3 | % 20.00 |
Midterms | 1 | % 30.00 |
Semester Final Exam | 1 | % 50.00 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 50 | |
PERCENTAGE OF FINAL WORK | % 50 | |
Total | % 100 |
SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE |
WORKLOAD OF TEACHING & LEARNING ACTIVITIES | |||
Teaching & Learning Activities | # of Activities per semester | Duration (hour) | Total Workload |
Course | 14 | 3 | 42 |
Laboratory | 0 | 0 | 0 |
Application | 0 | 0 | 0 |
Special Course Internship (Work Placement) | 0 | 0 | 0 |
Field Work | 0 | 0 | 0 |
Study Hours Out of Class | 0 | 0 | 0 |
Presentations / Seminar | 0 | 0 | 0 |
Project | 3 | 0 | 0 |
Homework Assignments | 0 | 0 | 0 |
Total Workload of Teaching & Learning Activities | - | - | 42 |
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES | |||
Assesment & Evaluation Activities | # of Activities per semester | Duration (hour) | Total Workload |
Quizzes | 0 | 0 | 0 |
Midterms | 1 | 3 | 3 |
Semester Final Exam | 1 | 3 | 3 |
Total Workload of Assesment & Evaluation Activities | - | - | 6 |
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities) | 48 | ||
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h) | 6 |