Machine Vision

1.

Subject title

Machine Vision

Машинска визија

2.

Code

F23L3W123

3.

Study program

Примена на информациски технологии, Софтверско инженерство и информациски системи, Компјутерски науки, Компјутерско инженерство, Интернет, мрежи и безбедност, Информатичка едукација, Software engineering and information systems, Примена на информациски технологии, Софтверско инженерство и информациски системи, Компјутерски науки, Компјутерско инженерство, Интернет, мрежи и безбедност, Software engineering and information systems, Стручни студии за програмирање, Стручни студии за програмирање, Software Engineering,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Прв циклус

6.

Academic year / semester

4 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Андреа Кулаков проф. д-р Ивица Димитровски

9.

Prerequisites for enrollment

120ЕКТС

10.

Subject goals and competencies:

To introduce students to basic concepts and techniques in computer vision. Students who will successfully finish the course they will be able to design efficient computer vision systems such as: handwriting recognition, face detection and recognition, motion estimation, people and vehicle tracking, gesture recognition, recognition and classification of visual objects, understanding and analysis of scenes etc.

11.

Subject content:

Lectures: 1. Introduction to Computer Vision 2. Cameras and optics. Lighting and color. 3. Pixels and filters. 4. Image processing in frequency domain. Pyramids in a picture. 5. Edge detection and line matching. 6. Significant points of interest. 7. Point of interest descriptors. 8. Trait matching and RANSAC 9. Deep learning 10. Application of deep learning. 11. Face recognition 12. Latest topics in Machine Vision.

12.

Learning methods:

предавања, аудиториски вежби, лабораториски вежби, проектни задачи, домашни задачи

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

30 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

45 hours

16.

Other forms of activities

16.1.

Project tasks

15 hours

16.2.

Independent tasks

15 hours

16.3.

Homework

75 hours

17.

Grading method

17.1.

Tests

30 points

17.2.

Seminar work / project (presentation: written and oral)

15 points

17.3.

Activities and learning

10 points

17.4.

Final exam

20 points

18.

Grading criteria (points / grade)

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.

Condition for signature and taking final exam

Реализирани активности 15, 16

20.

Language of instruction

македонски

21.

Quality assurance method

интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature