Delivered at:: Department of Applied Mathematics and Informatics (Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod))

Course type:: Compulsory course

When:: 1 year, 2 module

Instructor

Savchenko, Andrey

Full Syllabus

Abstract

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.

Learning Objectives

Learning the main algorithms of traditional image processing.
Thorough understanding of benefits and limitations of traditional image processing.

Expected Learning Outcomes

Apply image binarization techniques.
Apply local filters for image smoothing.
Apply object detection, image retrieval and/or image segmentation in practice.
Apply point-wise operations for image contrast enhancements.
Create panoramas with image stitching.
Detect objects with the Viola-Jones method.
Display video from web-camera and video files.
Distinguish image color models.
Distinguish the main tasks of computer vision and 2d image processing.
Find correspondent parts in different images.
Perform denoising in binary images with mathematical morphology.
Perform image processing operations for video frames.
Process grayscale and color images.
Segment objects in binary images.
Set-up environment (OpenCV library) for C++/Python development on Windows and Linux/MacOS.
Solve content-based image retrieval tasks.
Track object movements in videos.
Use FFT (Fast Fourier transform) for image filtering.

Course Contents

2D image processing overview
Basic operations of 2D image processing
Local (spatial) image filtering
Image matching and local descriptors
Image classification and object detection
2D image segmentation and object tracking. Final project

Assessment Elements

Weekly Quizzes
Programming Assignment 2 per course
Final exam
Testing assignment Programming assignment

Interim Assessment

2021/2022 2nd module
0.4 * Final exam + 0.2 * Weekly Quizzes + 0.4 * Programming Assignment 2 per course

Bibliography

Recommended Core Bibliography

Prince, S. J. D. (2012). Computer Vision : Models, Learning, and Inference. New York: Cambridge eText. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=458656

Recommended Additional 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

Master’s Programme 'Artificial Intelligence and Computer Vision'

Email

2D image processing