Machine Vision

Објавено: June 28, 2022
1. Course Title Machine Vision
2. Code 4ФЕИТ05Л023
3. Study program КХИЕ,КСИАР
4. Organizer of the study program (unit, institute, department) Faculty of Electrical Engineering and Information Technologies
5. Degree (first, second, third cycle) First cycle
6. Academic year/semester IV/8 7. Number of ECTS credits 6
8. Lecturer D-r Zoran Ivanovski
9. Course Prerequisites Passed: Signals and Systems
10. Course Goals (acquired competencies): The course should enable students to gain basic knowledge on theoretical and practical aspects of image analysis and machine vision. Upon successful completion of the course, students will understand basics of robust feature detection in images, understand various methods for registration, understand the basics of 2D and 3D machine vision, scene categorization and object detection. They will posses knowledge and practical skills that are necessary for implementing machine vision applications.
11. Course Syllabus: Basic concepts of image, image processing and machine vision. Image segmentation. Representation and description of images. Context recognition. Image retrieval. Automatic image annotation. Object description and recognition. Human figure and face recognition. Feature tracking and motion estimation. Image formation models. 3-D scene reconstruction from single view and multiple views. Structure from motion. Structure from focus, silhouettes and shadows.
12. Learning methods: Combined teaching method: lecturing, tutorials and lab exercises, supported by presentations and visualization of concepts, active participation of students through tests, assignments and projects.
13. Total number of course hours 3 + 1 + 1 + 0
14. Distribution of course hours 180
15. Forms of teaching 15.1. Lectures-theoretical teaching 45
15.2. Exercises (laboratory, practice classes), seminars, teamwork 30
16. Other course activities 16.1. Projects, seminar papers 30
16.2. Individual tasks 0
16.3. Homework and self-learning 75
17. Grading 17.1. Exams 10
17.2. Seminar work/project (presentation: written and oral) 20
17.3. Activity and participation 0
17.4. Final exam 70
18. Grading criteria (points) up to 50 points 5 (five) (F)
from 51to 60 points 6 (six) (E)
from 61to 70 points 7 (seven) (D)
from 71to 80 points 8 (eight) (C)
from 81to 90 points 9 (nine) (B)
from 91to 100 points 10 (ten) (A)
19. Conditions for acquiring teacher’s signature and for taking final exam Lectures and tutorials attendance and successful completion of lab exercises.
20. Forms of assessment During the semester, tests from laboratory exercises are provided (after the completion of each of the exercises). The student should also prepare a project assignment and submit it no later than the final exam. The final oral exam (duration 60 minutes) is taken in the planned exam sessions. The final grade includes the points from the tests from the laboratory exercises, the project task and the final oral 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 Szeliski Computer Vision: Algorithms and Applications Springer 2010
23.2. Additional Literature
No. Author Title Publisher Year
1 C. C. Aggarwal Neural Networks and Deep Learning Springer 2018