Mechatronics Engineering
Course Details
KTO KARATAY UNIVERSITY
Faculty of Engineering and Natural Science
Programme of Mechatronics Engineering
Course Details
Faculty of Engineering and Natural Science
Programme of Mechatronics Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 15571003 | Robot Vision | 2025 | Autumn | 7 | 3+0+0 | 3 | 5 |
| Course Type | Elective |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | Prof. Ali Bülent UŞAKLI |
| Instructor(s) | - |
| Instructor Assistant(s) | Res. Asst. Sinan İLGEN |
Course Content
Digital image processing, Numerical optimization, Video analysis, Optical flow, Image stitching, and Stereo vision.
Objectives of the Course
This course gives a comprehensive introduction to computer vision.
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 |
|---|---|---|
| P2 | Ability to identify, formulate and solve complex Mechatronics Engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose. | 5 |
| P3 | Ability to design a complex system, process, device, or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Ability to understand the basic methods of computer vision | P.2.25 | 1 |
| O2 | Ability to learn the Image Processing and Computer Vision toolbox in Matlab | P.3.8 | 1 |
| ** 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 Computer Vision |
| 3 | Introduction to digital image processing with Matlab I |
| 4 | Introduction to digital image processing with Matlab II |
| 5 | Solving the least squares problem |
| 6 | Background and foreground detection I |
| 7 | Background and foreground detection II |
| 8 | Optical flow |
| 9 | Midterm |
| 10 | Tracking Algorithms |
| 11 | Feature detection and matching |
| 12 | Image registration and image stitching |
| 13 | Stereo vision for depth estimation |
| 14 | Project Presentation |
Textbook or Material
| Resources | Richard Szeliski, Computer Vision: Algorithms and Applications, Springer, 2011. |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | 1 | 35 (%) |
| 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 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 5 | 70 |
| Midterms | 1 | 10 | 10 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 14 | 1 | 14 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 10 | 10 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 146 | ||
| Total Work Load / 30 | 4,87 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P2 | P3 |
|---|---|---|---|
| O1 | Ability to understand the basic methods of computer vision | 5 | - |
| O2 | Ability to learn the Image Processing and Computer Vision toolbox in Matlab | - | 5 |