Bachelor
2024/2025
Basics in computer vision
Type:
Compulsory course (Information and Communication Technologies and Systems)
Area of studies:
Infocommunication Technologies and Systems
Delivered by:
School of Electronic Engineering
When:
4 year, 1 module
Mode of studies:
distance learning
Online hours:
40
Open to:
students of all HSE University campuses
Instructors:
Sergey Vartanov
Language:
English
ECTS credits:
3
Contact hours:
6
Course Syllabus
Abstract
Computer vision (CV) is a field of computer science that focuses on enabling computers to understand and interpret visual data from images or video. It basically tries to replicate human vision capabilities in various visual tasks such as object detection and recognition, image classification, object tracking and so on. Computer Vision can be applied to a variety of applications like Autonomous Vehicles, Facial Recognition, Medical Imaging, and Robotics, to name a few. This course aims to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. It covers both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation.
Learning Objectives
- Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.
- Distinguish the main tasks of computer vision and 2d image processing.
- Understand various topics in computer vision, including image processing, feature extraction, object recognition, tracking, and autonomous driving.
- Be prepared for future internships, research projects, and job opportunities in the field of computer vision.
Expected Learning Outcomes
- Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.
- Distinguish the main tasks of computer vision and 2d image processing.
- Understand various topics in computer vision, including image processing, feature extraction, object recognition, tracking, and autonomous driving.
- 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
- Introduction to Computer Vision
- Image matching and classification. Content-based image retrieval
- Convolutional features for visual recognition
- Object detection
- Video analysis
- Image segmentation and synthesis
Bibliography
Recommended Core Bibliography
- Mathematical methods in computer vision, , 2003
- Компьютерное зрение : современный подход, Форсайт, Д., 2004
- Компьютерное зрение : учеб. пособие для студентов вузов, Шапиро, Л., 2006
- Коул Анирад, Ганджу Сиддха, Казам Мехер - Искусственный интеллект и компьютерное зрение. Реальные проекты на Python, Keras и TensorFlow. — (Серия «Бестселлеры O'Reilly») - 978-5-4461-1840-3 - Санкт-Петербург: Питер - 2023 - 386799 - https://ibooks.ru/bookshelf/386799/reading - iBOOKS
Recommended Additional Bibliography
- Computer vision : models, learning, and inference, Prince, S. J. D., 2012
- Computer vision for visual effects, Radke, R. J., 2013