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 |
|---|---|---|---|---|---|---|---|
| 05081480 | Computer Vision | 4 | Spring | 8 | 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 | Yok |
| Mode of Delivery | Face to Face |
| Prerequisites | Yok |
| Coordinator | - |
| Instructor(s) | Assoc. Prof. Ali ÖZTÜRK |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Assoc. Prof. Ali ÖZTÜRK | A-127 | [email protected] | Thursday 14.00-15.00 |
Course Content
Image Creation and Detection, Image Processing, Edge Detection, Reflection Map and Photometric Stereo
Objectives of the Course
Vision is our most powerful sense that allows us to interact with our environment without the need for physical contact. Through vision, we can learn the shape, condition and relationships of objects. The methods needed to bring these capabilities of the sense of vision to computers will be examined.
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 |
|---|---|---|
| P2 | Ability to identify, formulate, and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose | 4 |
| P6 | Ability to work effectively in disciplinary and multi-disciplinary teams; individual study skills | 2 |
| P10 | Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development | 3 |
| P11 | Knowledge of the effects of engineering practices on health, environment and safety in the universal and social dimensions and the problems of the era in engineering; awareness of the legal consequences of engineering solutions | 1 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Knowledge of algorithm design and analysis techniques. | P.3.1 | 2 |
| O2 | Knows information communication protocols and standards, can choose protocols | P.2.18 | 1 |
| O3 | Knowledge of processor structure and operating logic. | P.3.21 | 1 |
| O4 | Statistical evaluation techniques | P.2.24 | |
| ** 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 to computer vision; The functions of the computer vision system and its relationships with other areas |
| 2 | Image creation and detection; image creation with perspective projection, lenses, brightness, image perception; color detection, image quantization |
| 3 | Binary images and their geometric properties; binary images, simple geometric features; area and location, orientation, projections |
| 4 | Binary images and their topological properties; multiple objects; labeling components, linked objects |
| 5 | Regions and image segmentation; threshold level methods, determining regions with histogram, image segmentation, classification using color, segmentation by combining and splitting |
| 6 | Image Processing and Noise Removal |
| 7 | Edges and edge detection; edge detection methods in images |
| 8 | Midterm |
| 9 | 2D mapping |
| 10 | Stereo correspondence |
| 11 | Determining Shape from Reflection Map and Shadow |
| 12 | Recognition, Object Detection, Face Recognition.. |
| 13 | Project Presentations |
| 14 | Final |
Textbook or Material
| Resources | Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, Pearson International Edition, 2008, ISBN: 0-13-505267-x |
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 | 4 | 56 |
| 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 | 14 | 4 | 56 |
| Total Work Load: | 160 | ||
| Total Work Load / 30 | 5,33 | ||
| 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 | Knows information communication protocols and standards, can choose protocols | 2 | - |
| O2 | Statistical evaluation techniques | - | 1 |
| O3 | Knowledge of algorithm design and analysis techniques. | 4 | 5 |
| O4 | Knowledge of processor structure and operating logic. | 1 | - |