The course is devoted to the usage of computer vision libraries like OpenCV in 2d image processing. The course includes sections of image filtering and thresholding, edge/corner/interest point detection, local and global descriptors, video tracking.
Aim of the course:
• Learning the main algorithms of traditional image processing
• Thorough understanding of benefits and limitations of traditional image processing
Practical Learning Outcomes expected:
• Mastering programming skills of image processing with computer vision libraries
This Course is part of HSE University Master of Computer Vision degree program. Learn more about the admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/r381p.
2D image processing overview
In this module you will know the basic information about computer vision and image processing. The listener will learn how to set-up a real-time optimized Computer Vision library (OpenCV) for different computer languages. Some simple operations of video file processing will be presented.
Basic operations of 2D image processing
In this module the listener will know about basic operations of image processing such as working with different color models, normalization and binarization techniques, image contrast enhancements.
Local (spatial) image filtering
It this module the listener will learn different image filtering techniques and morphological operations. Edge and circle detection algorithms will be discussed and demonstrated in practice
This module contains final project of the course. The goal of this project to apply all knowledges from the previous weeks and implement a program that solve a certain task.
Alexander Smorkalov, Anastasiia Sokolova , Alexander Demidovskij and Andrey Savchenko