Infopaket

Technologies of computer vision

ВК2.9

Вибіркова дисципліна для ОП

First

2024/2025

8 Semester

4

Be able to use the methods of computational intelligence, machine learning, neural network and fuzzy data processing, genetic and evolutionary programming to solve problems of recognition, forecasting, classification, identification of control objects, etc. Know and apply methods of intelligent data analysis and artificial intelligence, including methods of computational linguistics, deep learning, neural network technologies and methods of computer vision. Be able to design and maintain modern hardware and software tools and components of image processing and computer vision systems, develop image processing and recognition systems based on modern software and automated design systems for electronic components of computer vision systems

Full-time form

Know higher mathematics, discrete mathematics, probability theory, statistics, information technology, expert systems, algorithm theory, algorithmization and programming, system modeling, intelligent data analysis, design of expert systems and decision support systems, architecture of modern information systems, methods and systems of parallel programming, decision-making methods, computer design technologies, theory of neural networks, intelligent data analysis. Have elementary skills of working with graphic editors, programming languages.

The discipline "Computer Vision Technologies" examines the physiological structure of the human visual channel, definitions and types of images, the main elements and characteristics of images, encoding and storage of images in a computer system. image compression methods, image processing, Fourier transform, wavelet transform, Hadamard, Hough, Haar, Hartley, and Radon transforms, image preprocessing methods and tools, image recognition methods, object identification in access systems, and moving objects objects, biometric identification, video surveillance systems, medical image processing and text recognition tools.

Pratt W.K. 2016. Digital Images Processing. Third edition. Wiley. (англ.) Solomon C. and T. Breckon. 2011. Fundamental of Digital Image Processing. A Practical Approach with Examples in Matlab. Wiley – Blackwell. (англ.) Stepan Bilan, SergeyYuzhakov. Image Processing and Pattern Recognition Based on Parallel Shift Technology.- CRC Press, Taylor & Francis Group,- 2018,- 194 p. (англ.)

Lectures, laboratory activities, individual work

The level of achievement of all planned learning outcomes is determined by the results of defense of laboratory work and individual tasks of independent work. Semester assessment of students is carried out during the semester for all types of work. The total score is formed as a weighted sum of points earned by the student for various types of work. The maximum number of points that a student can receive for work in a semester does not exceed 100 points. The final evaluation form is an exam. The exam is conducted by assigning a final grade, which is defined as the sum of points for all successfully evaluated learning outcomes. A student is not admitted to the exam if he scored less than 36 points during the semester.

Ukrainian