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Regular version of the site
Master 2022/2023

2D image processing

Type: Compulsory course (Master of Computer Vision)
Area of studies: Applied Mathematics and Informatics
Delivered by: Department of Applied Mathematics and Informatics
When: 1 year, 2 module
Mode of studies: distance learning
Online hours: 90
Open to: students of one campus
Instructors: Andrey Savchenko
Master’s programme: Master of Computer Vision
Language: English
ECTS credits: 6
Contact hours: 6

Course 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 Objectives

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

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

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

Assessment Elements

  • non-blocking Final exam
  • non-blocking Programming assignment
  • non-blocking Weekly Quizzes
Interim Assessment

Interim Assessment

  • 2022/2023 2nd module
    0.4 * Final exam + 0.2 * Weekly Quizzes + 0.4 * Programming assignment
Bibliography

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

Authors

  • DEMIDOVSKIY ALEKSANDR VLADIMIROVICH
  • SAVCHENKO ANDREY VLADIMIROVICH