Course Name | Computer Vision |
Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
---|---|---|---|---|---|
CE 466 | Fall/Spring | 3 | 0 | 3 | 5 |
Prerequisites | None | |||||
Course Language | English | |||||
Course Type | Elective | |||||
Course Level | First Cycle | |||||
Mode of Delivery | - | |||||
Teaching Methods and Techniques of the Course | ||||||
Course Coordinator | ||||||
Course Lecturer(s) | - | |||||
Assistant(s) | - |
Course Objectives | This course is designed to introduce fundamental principles and applications of computer vision. During the course, the fundamental concepts of computer vision will be discussed, real-world applications of computer vision will be described, and students will participate in a project where they will apply computer vision algorithms. |
Learning Outcomes | The students who succeeded in this course;
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Course Description | The following topics will be included: image formation, image processing, feature detection and matching, segmentation, feature-based alignment, structure from motion, dense motion estimation, image stitching, computational photography, stereo correspondence, 3D reconstructions, image-based rendering, and recognition. |
Related Sustainable Development Goals | |
| Core Courses | |
Major Area Courses | ||
Supportive Courses | ||
Media and Managment Skills Courses | ||
Transferable Skill Courses |
Week | Subjects | Required Materials |
1 | Introduction to Computer Vision | Chapter 1. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
2 | Image Formation | Chapter 2. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
3 | Image Processing | Chapter 3. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
4 | Feature Detection and Matching | Chapter 4. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
5 | Segmentation | Chapter 5. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
6 | Feature-Based Alignment | Chapter 6. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
7 | Structure From Motion | Chapter 7. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
8 | Dense Motion Estimation | Chapter 8. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
9 | Midterm exam | |
10 | Image Stitching | Chapter 9. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
11 | Computational Photography | Chapter 10. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
12 | Stereo Correspondence | Chapter 11. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
13 | 3D Reconstruction | Chapter 12. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
14 | Image-based Rendering | Chapter 13. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
15 | Recognition | Chapter 14. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
16 | Project presentations |
Course Notes/Textbooks | Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
Suggested Readings/Materials | Shapiro and Stockman, Computer Vision, Prentice-Hall, 2001; Deep Learning, by Goodfellow, Bengio, and Courville; Dictionary of Computer Vision and Image Processing, by Fisher et al. |
Semester Activities | Number | Weigthing |
Participation | ||
Laboratory / Application | ||
Field Work | ||
Quizzes / Studio Critiques | ||
Portfolio | ||
Homework / Assignments | ||
Presentation / Jury | ||
Project | 1 | 30 |
Seminar / Workshop | ||
Oral Exam | ||
Midterm | 1 | 30 |
Final Exam | 1 | 40 |
Total |
Weighting of Semester Activities on the Final Grade | 2 | 60 |
Weighting of End-of-Semester Activities on the Final Grade | 1 | 40 |
Total |
Semester Activities | Number | Duration (Hours) | Workload |
---|---|---|---|
Course Hours (Including exam week: 16 x total hours) | 16 | 3 | 48 |
Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | ||
Study Hours Out of Class | 14 | 2 | 28 |
Field Work | |||
Quizzes / Studio Critiques | |||
Portfolio | |||
Homework / Assignments | |||
Presentation / Jury | |||
Project | 1 | 30 | |
Seminar / Workshop | |||
Oral Exam | |||
Midterms | 1 | 20 | |
Final Exams | 1 | 24 | |
Total | 150 |
# | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
1 | To have adequate knowledge in Mathematics, Science, Computer Science and Software Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems. | X | ||||
2 | To be able to identify, define, formulate, and solve complex Software Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose. | X | ||||
3 | To be able to design, implement, verify, validate, document, measure and maintain a complex software system, process, or product under realistic constraints and conditions, in such a way as to meet the requirements; ability to apply modern methods for this purpose. | |||||
4 | To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in software engineering applications; to be able to use information technologies effectively. | X | ||||
5 | To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex Software Engineering problems. | |||||
6 | To be able to work effectively in Software Engineering disciplinary and multi-disciplinary teams; to be able to work individually. | |||||
7 | To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to be able to present effectively, to be able to give and receive clear and comprehensible instructions. | |||||
8 | To have knowledge about global and social impact of engineering practices and software applications on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Engineering and Software Engineering solutions. | |||||
9 | To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications. | |||||
10 | To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development. | |||||
11 | To be able to collect data in the area of Software Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1) | |||||
12 | To be able to speak a second foreign language at a medium level of fluency efficiently. | |||||
13 | To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Software Engineering. |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest