Mechatronics Engineering Master of Science
Course Details
KTO KARATAY UNIVERSITY
Graduate Education Institute
Programme of Mechatronics Engineering Master of Science
Course Details
Graduate Education Institute
Programme of Mechatronics Engineering Master of Science
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 81811119 | Industrial Image Processing | 1 | Autumn | 1 | 3+0+0 | 7 | 7 |
| Course Type | Elective |
| Course Cycle | - |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | Asst. Prof. Emre OFLAZ |
| Instructor Assistant(s) | Res. Asst. Sinan Ilgin |
Course Content
Görüntü Örnekleme ve Niceleme, Görüntü Geliştirme, Filtreleme, Renkli Görüntü İşleme ve Görüntü Segmentasyonu.
Objectives of the Course
The course is designed to give the students all the fundamental concepts in digital image processing
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P7 | Ability to propose innovative solution according to basic science and technological developments. | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Gain knowledge and practical experience in digital image processing | P.7.5 | |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Introduction and Familiarization with the teaching environment |
| 2 | Introduction to Digital Image Processing |
| 3 | Image enhancement in the spatial domain |
| 4 | Image smoothing and sharpening filters |
| 5 | Image enhancement in the frequency domain |
| 7 | Image restoration |
| 8 | Midterm |
| 9 | Geometric transformations |
| 10 | Color models |
| 11 | Fundamentals of image compression |
| 12 | Image segmentation techniques I |
| 13 | Image segmentation techniques II |
| 14 | Project Presentation |
Textbook or Material
| Resources | C. Solomon and T. Breckon, Fundamentals of Digital Image Processing A Practical Approach with Examples in Matlab, Wiley, 2010 |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | 1 | 35 (%) |
| Seminar | - | - |
| Quiz | - | - |
| Midterms | 1 | 30 (%) |
| Final Exam | 1 | 35 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 6 | 84 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 5 | 70 |
| Midterms | 1 | 15 | 15 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 14 | 1 | 14 |
| Project | 1 | 30 | 30 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 5 | 5 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 218 | ||
| Total Work Load / 30 | 7,27 | ||
| Course ECTS Credits: | 7 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P7 |
|---|---|---|
| O1 | Gain knowledge and practical experience in digital image processing | 5 |