Master
2023/2024
Computer Vision
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type:
Elective course (Machine Learning and Data Analysis)
Area of studies:
Applied Mathematics and Informatics
Delivered by:
Department of Informatics
When:
2 year, 1, 2 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Anton Kuznetsov
Master’s programme:
Machine Learning and Data Analysis
Language:
English
ECTS credits:
6
Contact hours:
44
Course Syllabus
Abstract
It is an elective discipline. The discipline is aimed at familiarizing students with the main tasks and methods of computer vision, such as image processing, image alignment and comparison, image classification, image search by content, object selection, object segmentation, image stylization, image synthesis, optical flow calculation, support of single and multiple targets, event recognition, 3D reconstruction from images. To master the discipline, students need to have knowledge gained as a result of studying the disciplines "Mathematical Analysis", "Linear Algebra and Geometry", "Probability Theory and Mathematical Statistics", "Machine Learning", "Algorithms and Data Structures".
Learning Objectives
- Formation of students' theoretical knowledge and practical skills on the basics of building high-performance graphics systems.
Expected Learning Outcomes
- Knows the mathematical apparatus of modern computer graphics; basic techniques and simplifications to achieve realistic computer graphics.
- Able to use modern algorithms for creating three-dimensional computer programs; apply various effects to achieve the realism of the displayed image.
- Has skills in solving basic problems of computer graphics using various algorithms; skills in the development of modern applications with intensive use of computer graphics methods.
- Knows algorithms for image synthesis. Owns the concept of color in computer graphics. Owns the concepts of the pyramid of visibility; projective transformations; graphics pipeline. Knows texturing.
Course Contents
- Section 1. Introduction. Color, texturing
- Section 2. Lighting models. Shadows. Effects based on particle systems
- Section 3. Image smoothing. Photometry and radiometry.
- Section 4. Relief modeling. Text rendering. Animation.
Assessment Elements
- Course projectThe course project is issued to students in three versions. Deadline - 3 weeks. The form of presentation by students of the course project is an application for Android.
- ExamThe exam is conducted in the form of answers to the questions of the examination ticket. The examination ticket is formed from two random questions from the list of questions for the exam. 1 hour is allotted for answering.
Bibliography
Recommended Core Bibliography
- OpenCV и Java. Обработка изображений и компьютерное зрение - 978-5-9775-3955-5 - Прохоренок Н.А. - 2018 - Санкт-Петербург: БХВ-Петербург - https://ibooks.ru/bookshelf/358884 - 358884 - iBOOKS
- Баранов С.Н., Толкач С.Г. - Основы компьютерной графики - Сибирский федеральный университет - 2018 - ISBN: 978-5-7638-3968-5 - Текст электронный // ЭБС ZNANIUM - URL: https://znanium.com/catalog/document?id=342164
- Колошкина, И. Е. Компьютерная графика : учебник и практикум для вузов / И. Е. Колошкина, В. А. Селезнев, С. А. Дмитроченко. — 3-е изд., испр. и доп. — Москва : Издательство Юрайт, 2023. — 233 с. — (Высшее образование). — ISBN 978-5-534-12341-8. — Текст : электронный // Образовательная платформа Юрайт [сайт]. — URL: https://urait.ru/bcode/513030 (дата обращения: 27.08.2024).
Recommended Additional Bibliography
- Vince, J. (2006). Mathematics for Computer Graphics (Vol. 2nd ed). London: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=150552