| 1. | Course Title | Machine Vision | |||||||||||
| 2. | Code | 4ФЕИТ05016 | |||||||||||
| 3. | Study program | 10-DPSM, 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 Tomislav Kartalov | |||||||||||
| 9. | Course Prerequisites | ||||||||||||
| 10. | Course Goals (acquired competencies):
The goal of the curriculum is to enable students to acquire an expanded knowledge of the theoretical and practical aspects of image analysis and machine vision. Upon successful completion of the course, students will understand the theoretical basis, algorithms, and capabilities of the robust feature extraction in images, and further use of those features in object recognition, human figure and face recognition, and context recognition. Apart from understanding the algorithms, the students will also be trained in the appropriate preparation of the data sets, their annotation and augmentation, as well as the impact of these procedures on the success of a certain algorithm. Apart from the theoretical basis, students will also acquire practical skills necessary for research, development and implementation of applications in the field of machine vision. |
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| 11. | Course Syllabus:
Basic terms and definitions of image, image processing and machine vision. Image formation. Image processing. Image representation and description. Feature extraction. Object recognition. Human figure and face recognition. Context recognition. Image segmentation. Searching large image sets. Automatic annotation. Feature tracking and motion estimation. |
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| 12. | Learning methods:
Lectures, project assignments, presentations |
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| 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 | Finished project assignment or seminar work | |||||||||||
| 20. | Forms of assessment | Written and oral | |||||||||||
| 21. | Language | Macedonian and English | |||||||||||
| 22. | Method of monitoring of teaching quality | Self-evaluation, surveys, questionnaires. | |||||||||||
| 23. |
Literature |
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| 23.1. | Required Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | Richard Szeliski | Computer vision: algrorithms and applications – second edition | Springer Nature Switzerland AG | 2022 | |||||||||
| 23.2. | Additional Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | Richard Hartley, Andrew Zisserman | Multiple View Geometry in Computer Vision, 2nd edition | Cambridge University Press | 2004 | |||||||||
