2D image processing

2024/2025

Учебный год

ENG

Обучение ведется на английском языке

6

Кредиты

Кто читает:

Кафедра информационных систем и технологий (Нижний Новгород) (Факультет информатики, математики и компьютерных наук (Нижний Новгород))

Статус:

Курс обязательный

Когда читается:

1-й курс, 2 модуль

Преподаватель

Савченко Андрей Владимирович

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 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

• Programming assignment
• Final exam
• Weekly Quizzes

Interim Assessment

• 2024/2025 2nd module
  0.4 * Final exam + 0.4 * Programming assignment + 0.2 * Weekly Quizzes

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 Andrei Vladimirovich