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Basics in Computer Vision
Use the special code ‘ХОЧУВВЫШКУ’ (‘I want to study at HSE’) to get free access to the track.
This track is aimed at a wide range of specialists who want to get acquainted with computer vision. Students will learn the mathematical and programming skills necessary to develop algorithms in the field of computer vision and how to use the OpenCV library to analyse two-dimensional images. The OpenCV library is widely used by developers of computer vision applications, so students will be able to apply the skills acquired in this track to their real practical activities.
OBJECT-ORIENTED PROGRAMMING
This course is primarily intended for students with basic programming knowledge who want to develop their C++ programming skills and learn about OOP in the context of C++. Students will learn about the advantages and features of OOP over procedural programming, as well as become familiar with the standard template library and CMake, a tool for building, testing, and packaging software.
Instructors: Vasily Shamporov, Egor Churaev
Aims of the course:
- Learn about OOP in the context of C++
- Get familiar with CMake, STL, OpenCV
Practical learning outcomes:
- Improve C ++ programming skills
- Use CMake for project building
- Gain experience with the OpenCV library
Skills:
- Standard Template Library
- CMake;
- Object-Oriented Programming (OOP)
- C++
- Computer Programming
2D IMAGE PROCESSING
This course is devoted to the use of computer vision libraries like OpenCV in 2D image processing. The course includes sections on image filtering and thresholding, edge/corner/interest point detection, local and global descriptors, and video tracking.
Instructors: Andrey Savchenko, Alexander Smorkalov, Alexander Demidovskij, Anastasiia Sokolova
Aims of the course:
- Learn the main algorithms of traditional image processing
- Gain a thorough understanding of the benefits and limitations of traditional image processing
Practical learning outcomes:
- Master programming skills for image processing with computer vision libraries
MATHEMATICS FOR COMPUTER VISION
This course is devoted to the systematisation of mathematical knowledge necessary to master educational disciplines in the field of computer vision. The course includes sections on mathematical analysis, probability theory, and linear algebra.
Instructors: Valeriy Kalyagin, Sergey Slashchinin
Aims of the course:
- Systematise mathematical knowledge
- Prepare to use mathematical knowledge as a professional in the field of computer vision
Practical learning outcomes:
- Master practical skills in mathematics
- Solve the kind of mathematical problems encountered in the practical work of computer vision specialists