Machine Vision

Ivica DimitrovskiAndrea KulakovPetre Lameski
Human eye surrounded by computational data

# Machine Vision

# Overview

# Contents

  • Introduction to computer vision
  • Cameras and optics
  • Brightness and color
  • Pixels and filters
  • Image processing in frequency domain
  • Image pyramid
  • Machine learning:
    • Clustering and classification
    • Edge detection and line overlapping
    • Robust line overlapping (Hough transformation, RANSAC, etc.)
    • Clustering and image segmentation
    • Gaussian Mixture Models (GMM)
    • Points of interest detection
    • Feature tracking
    • Optical flow
    • Stereo correspondence
    • Scaling- and rotation-invariant feature transformation (SIFT, SURF)
    • Visual words dictionaries
    • Recognition and classification of visual objects

# Testimonials

2021 Maria Schildbeck

The course consisted of a theoretical part and a practical part. The lectures were 2 hours (theory) and 1 hour (programming exercises) per week. All material and lectures were in English. From October to mid-December we learnt the basic image processing, pixel manipulations, filters, edge detection, feature matching, etc. From mid-December onward we learnt about deep learning. There are no Christmas holidays in Macedonia, so be prepared to have lectures during this time. We had two exams in mid-December: The theoretical exam was two questions to answer essay-like, 30 minutes time. The practical exam was one problem solving task (programming) for which we had 24 hours and it had a workload of approx. 2 to 3 hours (keep in mind you probably have other lectures or exercises during this 24 hours). Only the students who passed the two exams could make a seminar work. It was within a deep learning topic recommended by the lecturer; you could also choose your own topic.

The grading structure was:

  • 20% theoretical exam
  • 30% practical exam
  • 50% seminar work

Overall, this course gave a complete introduction to image processing and deep learning. For the programming we used Jupyter Lab, OpenCV and PyTorch.

# Outcomes

The goal of this course is to introduce the students to the basic concepts and principles of computer vision. The students who will successfully finish the course will be able to

  • Design efficient systems for computer vision for handwriting recognition
  • Detection and recognition of faces
  • Movement detection
  • Human and vehicle tracking
  • Gesture recognition
  • Classification and recognition of visual objects
  • Scene analysis and understanding
  • Etc.

# Methods & Schedule

The course consists of a theoretical part and a practical part. The lectures are 2 hours (theory) and 1 hour (programming exercises) per week during the day and will be provided in hybrid mode (on-campus lecture + webinar room). Additionally, there will be some recordings of the lectures for watching at a later time.

Lectures using presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, invited guest lecturers, independent preparation and defense of a project assignment and seminar work.

Type Effort [h]
Lectures 30
Exercises 45
Project Tasks 15
Independent Learning Tasks 15
Home Learning 75

# Materials

Author Title Publisher Year
Richard Szeliski Computer Vision: Algorithms and Applications Microsoft Research 2010
D.A. Forsyth and J. Ponce Computer Vision: A Modern Approach Prentice Hall 2002
N. Sebe, M.S. Lew Robust Computer Vision: Theory and Applications (Computational Imaging and Vision) Springer 2003

# Assessment

The assessment consists of a practical assignment and a final exam, which is sufficient to pass the course. After that, you can do a seminar work/project (optional) from January to March to improve the grade. Please consider the additional effort which might be during your summer semester.

# Methods

Type Points/Percent
Tests (assignments) 30 points
Seminar paper / Project 40 points
Activity and Learning 10 points
Final exam (theory) 20 points

# Criteria

Grade Grade (letter) Scale
5 F up to 50 points
6 E 51 to 60 points
7 D 61 to 70 points
8 C 71 to 80 points
9 B 81 to 90 points
10 A 91 to 100 points

# Requirements

# Level


# Skills

# Equipment

  • Computer
  • Webcam

# Enrollment

Participation is free of charge. Student of partner universities can send applications to participate in courses.


# Ss. Cyril and Methodius University Skopje

Image of UKIM's University Campus

The Ss. Cyril and Methodius University in Skopje (UKIM) is the first and biggest public University in the Republic of North Macedonia, founded in 1949. At the moment, the University represents a functional community of 23 faculties, 5 research institutes, 4 public scientific institutions - associate members, 1 associate member - other higher education institutions and 7 associate members - other organizations. Its activities are stipulated by the Law on Higher Education and the Statute of the University.

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# Lecturers

Ivica Dimitrovski
Portrait of Ivica Dimitrovski

# Ivica Dimitrovski

Ivica was born in 1981 in Kratovo, Macedonia. In 2000, he started his studies with the Faculty of Electrical Engineering and Information Technologies, Saints Cyril and Methodius University of Skopje. He received the bachelor’s degree in computer science, automation and electrical engineering from the Faculty of Electrical Engineering and Information Technologies in 2005. In 2008, he received the M.Sc. degree and the Ph.D. degree in 2011.

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Andrea Kulakov
Portrait of Andrea Kulakov

# Andrea Kulakov

Andrea finished the secondary school in 1990, at age of 16, as he managed twice to take two school years during one year (7th and 8th grade in primary school and then 3rd and 4th year in high school). After that, he enrolled at the Faculty of Electrical Engineering in Skopje, Macedonia at the Computer Science Department and in 1995 graduated with the highest average grade (9.91 out of 10.00) in the generation 1990-95.

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Petre Lameski
Portrait of Petre Lameski

# Petre Lameski

Petre was born in 1985 in Kavadarci.

In 2008 he graduated at the Faculty of Electrical Engineering and Information Technologies at the University of Sts Cyril and Methodius in Skopje. In 2010 he finished his master studies at the same faculty with a thesis in the area of Robotics. From September 2008 untill September 2011 he worked as an assistant at the same faculty, teaching auditory and labaratory exercises to students in Introduction to Robotics, Distributed Computer Systems, Artificial Intelligence, Algorithms for Data Analysis, Information Systems and Mobile Information Systems.

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