Delivered at:: Big Data and Information Retrieval School

Course type:: Elective course

When:: 4 year, 3 module

Instructors

Загоруйко Сергей Николаевич

Копылов Иван Станиславович

Full Syllabus

Abstract

The course is devoted to the main tasks and methods of computer vision, such as image processing and classification, image search by content, object selection and segmentation, image stylization, image synthesis, optical flow calculation, single and multiple target tracking, event recognition. To master the academic discipline, students must possess the knowledge and competencies of the following disciplines: Mathematical analysis; Linear algebra and geometry; Theory of Probability and Mathematical Statistics; Fundamentals and methodology of programming; Algorithms and data structures; Machine learning 1; Machine learning 2; Introduction to Deep Learning

Learning Objectives

The main purpose of this course is to provide students with a comprehensive understanding of computer vision concepts, tools, and techniques, as well as practical experience through hands-on assignments. The course aims to equip students with the necessary skills and knowledge to excel in computer vision-related careers, such as machine learning, and prepare them for future research projects and job opportunities in the field.

Expected Learning Outcomes

Understand various topics in computer vision, including image processing, feature extraction, object recognition, tracking, and autonomous driving.
Gain practical experience through assignments such as panorama stitching, face recognition, and descriptors for image retrieval.
Develop critical thinking and problem-solving skills through the coursework and discussions.
Be prepared for future internships, research projects, and job opportunities in the field of computer vision.
Understand transformers and multimodal computer vision concepts.
Apply the knowledge and skills gained in the course to real-world computer vision applications.

Course Contents

Object detectors
Image segmentation
Image conversion and generation
Video Processing Basics
Image Processing
Feature Extraction
Object Recognition
Tracking
Autonomous Driving
Transformers
Multimodal Computer Vision
Assignments
Problem-solving and Critical Thinking
Real-world Applications

Assessment Elements

Homework
Project

Interim Assessment

2023/2024 3rd module
0.5 * Homework + 0.5 * Project

Bibliography

Recommended Core Bibliography

Richard Szeliski. (2010). Computer Vision: Algorithms and Applications. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.C0E46D49

Recommended Additional Bibliography

Deep learning, Goodfellow, I., 2016

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