Infopaket

Pattern recognition and computer vision

ОК08

Обов’язкова дисципліна для ОП

Second

2023/2024

1 Semester

5

Be able to develop a conceptual model of an information or computer system, mathematical models and methods of data analysis, and develop algorithmic and software for data analysis. Know the construction principles, structure and architecture of computer pattern recognition systems. Be able to apply methods and means of pattern recognition. To have the principles of building multi-agent systems; use specialized tools for their implementation. Know the principles of creating knowledge bases and ontologies, means of designing semantic systems.

Distance form

Know higher mathematics, discrete mathematics, probability theory, statistics, information technologies, 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. Be able to work with modern software, database management systems, process and analyze signals and data arrays, design modern digital information processing tools. Have basic skills of working with graphic editors.

The discipline "Pattern recognition and computer vision" examines the physiological structure of the human visual channel, the basis of the theory and methods of image recognition, types, main elements and characteristics of images, presentation, encoding and storage of images in a computer system, methods of image compression, methods of image processing and transformation, characteristic features of images, methods and means of image preprocessing and recognition, the general structure of the image recognition system, identification of objects in access systems, identification of moving objects, biometric identification of a person, video surveillance systems, processing of medical images and text recognition tools.

Pratt W.K. 2016. Digital Images Processing. Third edition. Wiley Gonzalez R.C. and Woods R.E. 2008. Digital Image Processing. 3rd ed., Prentice Hall, New Jersey. Stepan Bilan, SergeyYuzhakov. Image Processing and Pattern Recognition Based on Parallel Shift Technology.- CRC Press, Taylor & Francis Group,- 2018,- 194 p. Stepan Bilan, Mykola Bilan, Ruslan Motornyuk, Serhii Yuzhakov. Biometric Data in Smart Cities: Methods and Models of Collective Behavior. CRC Press; 1st edition (July 19, 2021), 228 pages Parker J. Algorithms for Image Processing and Computer Vision. Wiley. -2011.

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. To pass the exam, it is mandatory to complete all laboratory work (minimum score - 27 points, maximum - 45 points), independent work (minimum score - 9 points, maximum - 15 points). The condition for receiving a positive final grade for a discipline is to achieve at least 60% of the maximum possible number of points, i.e. 60 points.

Ukrainian