BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, NONTHESIS) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
Course Introduction and Application Information
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CMP5126 | Image and Video Processing | Fall | 3 | 0 | 3 | 12 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Basic information
| Language of instruction: | English |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Assist. Prof. LAVDİE RADA ÜLGEN |
| Recommended Optional Program Components: | None |
| Course Objectives: | Digital image processing consists of understanding and gaining knowledge about the fundamentals of digital image processing, image transformation, image enhancement techniques, image restoration techniques, image compression, and segmentation. Students should obtain an intuitive understanding of the implementation of basic sciences and mathematics in solving image processing problems. Students should be able to produce skillful analyses, and design and develop a system/component/ process for a required needs analysis and interpretation of a given digital image data. Students should gain some experience in the implementation of image processing methods by using MATLAB. The course trains the students to approach multidisciplinary engineering challenges involving image processing and prepares them to compete for a successful career in the engineering profession through global education standards. |
Learning Outcomes
|
The students who have succeeded in this course; The students who have succeeded in this course will be able to: 1. learn and understand the image representation and imaging systems; 2. fundamentals of digital image processing and image representation; 3. learn and understand image enhancement techniques; 4. learn and understand image restoration techniques and methods; 5. learn and understand morphological image processing manipulation; 6. learn and understand image segmentation and registration; 7. learn and understand image compression; 8. code in Matlab real-life-related problems. |
Course Content
| This course is essential for engineering programs as it equips students with practical skills in image analysis, mathematical modeling, and computational techniques. Students learn to solve complex systems, perform error analysis, and apply interpolation, curve fitting, and least-squares methods, which are vital for data analysis and optimization in engineering. The course covers numerical differentiation, integration, and Fourier transformations, essential for fields like signal processing and fluid mechanics. Additionally, students gain experience in eigenvalue problems and singular value decomposition, relevant for control systems and image processing. Through MATLAB, students implement and test numerical methods, enhancing their problem-solving abilities in real-world engineering applications. |
Weekly Detailed Course Contents
| Week | Subject | Related Preparation |
| 1) | Introduction to image processing | |
| 2) | Image representation and imaging systems | |
| 3) | Fundamentals of image processing | |
| 4) | Image Enhancement - Histogram Modeling | |
| 5) | Image enhancement in the frequency domain | |
| 6) | Model of Image Degradation/ Restoration process | |
| 7) | Midterm | |
| 8) | Morphological image F2F processing | |
| 9) | Blurring and Deblurring | |
| 10) | Image Segmentation: Detection of discontinuities | |
| 11) | Color image processing | |
| 12) | Different application of image processing | |
| 13) | Basics of analog and digital video | |
| 14) | Revision and project presentation |
Sources
| Course Notes / Textbooks: | 1. Rafael C Gonzalez, Richard E Woods, "Digital Image Processing" - 2nd Edition, Pearson Education 2003 2. Geoff Dougherty 'Digital Image Processing for Medical Applications' Cambridge University Press, 2009 |
| References: | 1. T2. Jain A.K., "Fundamentals of Digital Image Processing", Pearson education 2. William K Pratt, "Digital Image Processing", John Willey 2001 3. Millman Sonka, Vaclav Hlavac, Roger Boyle, Broos/Colic, "Image Processing Analysis and Machine Vision" - Thompson Learning, 1999. 4. Chanda S., Dutta Majumdar - "Digital Image Processing and Applications", Prentice Hall of India, 2000 |
Evaluation System
| Semester Requirements | Number of Activities | Level of Contribution |
| Quizzes | 2 | % 10 |
| Homework Assignments | 1 | % 15 |
| Project | 1 | % 35 |
| Midterms | 1 | % 10 |
| Final | 1 | % 30 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 35 | |
| PERCENTAGE OF FINAL WORK | % 65 | |
| Total | % 100 | |
Contribution of Learning Outcomes to Programme Outcomes
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |