| 1. | Course Title | 3D Machine Vision | |||||||||||
| 2. | Code | 4ФЕИТ05001 | |||||||||||
| 3. | Study program | 6-ARSI,19-MV | |||||||||||
| 4. | Organizer of the study program (unit, institute, department) | Faculty of Electrical Engineering and Information Technologies | |||||||||||
| 5. | Degree (first, second, third cycle) | Second cycle | |||||||||||
| 6. | Academic year/semester | I/1 | 7. | Number of ECTS credits | 6.00 | ||||||||
| 8. | Lecturer | Dr Zoran Ivanovski | |||||||||||
| 9. | Course Prerequisites | ||||||||||||
| 10. | Course Goals (acquired competencies):
The goal of the course is to enable the students to study of the theoretical basis and practical aspects of 3D machine vision. Upon successful completion of the course the students will acquire the necessary knowledge for theoretical analysis, design and implementation of 3D machine vision algorithms, as well as necessary skills for testing and deployment of applications based on designed algorithms. They will be able to perform research, to use relevant literature and to follow new developments in the field of 3D machine vision. |
||||||||||||
| 11. | Course Syllabus:
Image formation models. Single view geometry and 3D scene reconstruction from single view. Two view geometry. Scene structure computation. N view geometry. Autocalibration. Structure from motion. Structure from focus and shadows. 3D point clouds. Point cloud formation. Traditional point cloud analysis and recognition. Deep learning-based point cloud recognition. |
||||||||||||
| 12. | Learning methods:
Combined learning methods: lectures, supported by presentations and concepts visualizations, active participation of students through project works. |
||||||||||||
| 13. | Total number of course hours | 180 | |||||||||||
| 14. | Distribution of course hours | 3 + 3 | |||||||||||
| 15. | Forms of teaching | 15.1 | Lectures-theoretical teaching | 45 hours | |||||||||
| 15.2 | Exercises (laboratory, practice classes), seminars, teamwork | 45 hours | |||||||||||
| 16. | Other course activities | 16.1 | Projects, seminar papers | 30 hours | |||||||||
| 16.2 | Individual tasks | 30 hours | |||||||||||
| 16.3 | Homework and self-learning | 30 hours | |||||||||||
| 17. | Grading | ||||||||||||
| 17.1 | Exams | 0 points | |||||||||||
| 17.2 | Seminar work/project (presentation: written and oral) | 50 points | |||||||||||
| 17.3. | Activity and participation | 0 points | |||||||||||
| 17.4. | Final exam | 50 points | |||||||||||
| 18. | Grading criteria (points) | up to 50 points | 5 (five) (F) | ||||||||||
| from 51 to 60 points | 6 (six) (E) | ||||||||||||
| from 61 to 70 points | 7 (seven) (D) | ||||||||||||
| from 71 to 80 points | 8 (eight) (C) | ||||||||||||
| from 81 to 90 points | 9 (nine) (B) | ||||||||||||
| from 91 to 100 points | 10 (ten) (A) | ||||||||||||
| 19. | Conditions for acquiring teacher’s signature and for taking final exam | Completed project work. | |||||||||||
| 20. | Forms of assessment |
The student should complete the project work and present it before the final exam. The final exam takes place in the exam session, the time frame is 60 minutes. The final score is formed based on the score from the project work and the final exam. |
|||||||||||
| 21. | Language | Macedonian and English | |||||||||||
| 22. | Method of monitoring of teaching quality | Internal evaluation and surveys. | |||||||||||
| 23. | Literature | ||||||||||||
| 23.1. | Required Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | Richard Hartley, Andrew Zisserman | Multiple View Geometry in Computer Vision | Cambridge University Press | 2003 | |||||||||
| 2. | Shan Liu, Min Zhang, Pranav Kadam, C.-C. Jay Kuo | 3D Point Cloud Analysis | Springer Nature Switzerland | 2021 | |||||||||
