Computer Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 05071909 | Robot Vision | 4 | 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 | - |
| Instructor(s) | Asst. Prof. Ali Osman ÇIBIKDİKEN |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. Ali Osman ÇIBIKDİKEN | A-124 | [email protected] | 07585 | Monday 14.00-15.00 |
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 | |
| Support Courses | X |
| 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 |
|---|---|---|
| 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 | 1 |
| P5 | An ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or disciplinary research topics | 3 |
| P10 | Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development | 4 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Must know power calculations | P.2.2 | |
| O2 | Knowledge of algorithm design and analysis techniques. | P.3.1 | |
| O3 | Knowledge of algorithm design and analysis techniques. | P.2.14 | |
| O4 | Ability to use information and communication technologies in the design process | P.3.13 | |
| O5 | Algorithm | P.2.23 | |
| ** 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 I |
| 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 | - | - |
| Quiz | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| 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 | 3 | 3 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 3 | 3 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 118 | ||
| Total Work Load / 30 | 3,93 | ||
| Course ECTS Credits: | 4 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P2 | P3 |
|---|---|---|---|
| O1 | Must know power calculations | 4 | - |
| O2 | Knowledge of algorithm design and analysis techniques. | 1 | - |
| O3 | Algorithm | - | 2 |
| O4 | Knowledge of algorithm design and analysis techniques. | - | 5 |
| O5 | Ability to use information and communication technologies in the design process | - | - |