ECTS - Advanced Topics in Digital Image Processing
Advanced Topics in Digital Image Processing (MDES672) Course Detail
Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
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Advanced Topics in Digital Image Processing | MDES672 | 3 | 0 | 0 | 3 | 5 |
Pre-requisite Course(s) |
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Consent of the Instructor |
Course Language | English |
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Course Type | N/A |
Course Level | Ph.D. |
Mode of Delivery | Face To Face |
Learning and Teaching Strategies | Lecture. |
Course Lecturer(s) |
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Course Objectives | Upon successful completion of the course, students will learn and have an understanding of the mathematical tools for modeling and analysis of image acquisition and processing systems. Students will also master basic techniques of image processing applications. |
Course Learning Outcomes |
The students who succeeded in this course;
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Course Content | Review of image processing fundamentals, frequency and space domain image processing methods; wavelets, multiresolution processing, and orthogonal transforms; image and video compression standards; image segmentation and representation; nonlinear image processing methods. |
Weekly Subjects and Releated Preparation Studies
Week | Subjects | Preparation |
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1 | Review of image processing fundamentals | Related pages of lecture notes and other sources |
2 | Review of basic frequency and space domain image processing techniques | Related pages of lecture notes and other sources |
3 | Review of basic frequency and space domain image processing techniques | Related pages of lecture notes and other sources |
4 | Subband decomposition, filter banks, and pyramids | Related pages of lecture notes and other sources |
5 | Subband decomposition, filter banks, and pyramids | Related pages of lecture notes and other sources |
6 | Wavelets and discrete orthogonal transforms | Related pages of lecture notes and other sources |
7 | Wavelets and discrete orthogonal transforms | Related pages of lecture notes and other sources |
8 | Nonlinear image processing techniques | Related pages of lecture notes and other sources |
9 | Nonlinear image processing techniques | Related pages of lecture notes and other sources |
10 | Image segmentation | Related pages of lecture notes and other sources |
11 | Image representation and Description | Related pages of lecture notes and other sources |
12 | Image representation and Description | Related pages of lecture notes and other sources |
13 | Object Recognition | Related pages of lecture notes and other sources |
14 | Object Recognition | Related pages of lecture notes and other sources |
15 | Overall review | - |
16 | Final exam | - |
Sources
Course Book | 1. Ders notları / Notes are available |
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Other Sources | 2. 1. Digital Image Processing, Rafael C. Gonzales and Richard E. Woods, Addison-Wesley Publishing Company, 1993. |
3. 2. Digital Video Processing, A. Murat Tekalp, Prentice-Hall, 1995. | |
4. 3. Two-Dimensional Signal and Image Processing, Jae S. Lim, Prentice-Hall, 1989. | |
5. 4. Fundamentals of Digital Image Processing, Anil K. Jain and Thomas Kailath, Prentice-Hall 1988. | |
6. 5. Digital Image Processing, Kenneth R. Castleman, Prentice-Hall, 1995. |
Evaluation System
Requirements | Number | Percentage of Grade |
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Attendance/Participation | - | - |
Laboratory | - | - |
Application | - | - |
Field Work | - | - |
Special Course Internship | - | - |
Quizzes/Studio Critics | 4 | 20 |
Homework Assignments | - | - |
Presentation | - | - |
Project | 2 | 40 |
Report | - | - |
Seminar | - | - |
Midterms Exams/Midterms Jury | 2 | 20 |
Final Exam/Final Jury | 1 | 20 |
Toplam | 9 | 100 |
Percentage of Semester Work | 80 |
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Percentage of Final Work | 20 |
Total | 100 |
Course Category
Core Courses | |
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Major Area Courses | X |
Supportive Courses | |
Media and Managment Skills Courses | |
Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
# | Program Qualifications / Competencies | Level of Contribution | ||||
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1 | 2 | 3 | 4 | 5 | ||
1 | Gains the ability to understand and apply knowledge in the fields of mathematics, science and basic sciences at the level of expertise. | |||||
2 | Gains the ability to access wide and deep knowledge in the field of Engineering by doing scientific research with current techniques and methods, evaluate, interpret and implement the gained knowledge. | |||||
3 | Being aware of the latest developments his/her field of study, defines problems, formulates and develops new and/or original ideas and methods in solutions. | |||||
4 | Designs and applies theoretical, experimental, and model-based research, analyzes and interprets the results obtained at the level of expertise. | |||||
5 | Gains the ability to use the applications, techniques, modern tools and equipment in his/her field of study at the level of expertise. | |||||
6 | Designs, executes and finalizes an original work process independently. | |||||
7 | Can work in interdisciplinary and interdisciplinary teams, lead teams, use the information of different disciplines together and develop solution approaches. | |||||
8 | Pays regard to scientific, social and ethical values in all professional activities and acquires responsibility consciousness at the level of expertise. | |||||
9 | Contributes to the literature by communicating the processes and results of his/her academic studies in written form or orally in national and international academic environments, communicates effectively with communities and scientific staff working in the field of specialization. | |||||
10 | Gains the skill of lifelong learning at the level of expertise. | |||||
11 | Communicates verbally and in written form using a foreign language at least at the European Language Portfolio B2 General Level. | |||||
12 | Recognizes the social, environmental, health, safety, legal aspects of engineering applications, as well as project management and business life practices, being aware of the limitations they place on engineering applications. |
ECTS/Workload Table
Activities | Number | Duration (Hours) | Total Workload |
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Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
Laboratory | |||
Application | |||
Special Course Internship | |||
Field Work | |||
Study Hours Out of Class | 16 | 2 | 32 |
Presentation/Seminar Prepration | |||
Project | |||
Report | |||
Homework Assignments | 4 | 5 | 20 |
Quizzes/Studio Critics | |||
Prepration of Midterm Exams/Midterm Jury | 2 | 8 | 16 |
Prepration of Final Exams/Final Jury | 1 | 10 | 10 |
Total Workload | 126 |