Infopaket

Digital information processing

ДВС.2.05

Обов’язкова дисципліна для ОП

First

2022/2023

7 Semester

4

PLO 5. Be able to develop and use in practice algorithms related to the approximation of functional dependencies, numerical differentiation, and integration, solving systems of algebraic, differential, and integral equations, solving boundary value problems, and finding optimal solutions. PLO 10. Build algorithms that are efficient in terms of calculation accuracy, stability, speed, and cost of system resources for the numerical study of mathematical models and solving practical problems. PLO 14. Use specialized software products and software systems of computer mathematics in practical work. PLO 21. Demonstrate professional communication skills, including oral and written communication in Ukrainian and at least one of the common European languages. PLO 23.2. PLO 26.2. http://csc.knu.ua/media/filer_public/6a/29/6a29dc9d-47c9-46ef-aacd-67e1d899140e/opp_pm_2018__1.pdf

Full-time form

To successfully study the discipline "Digital information processing " the student must meet the following requirements: 1. Know: 1) Fundamentals of mathematical analysis, linear algebra, discrete mathematics, differential equations, operations research, and numerical methods. 2) Image processing software. 2. Be able to: 1) Apply basic algorithms for filtering, restoration, and recognition in information processing. 2) Apply algorithms for digital information processing. 3. Possess: 1) Skills in construction, analysis, and application of mathematical methods in solving problems of image processing.

The purpose of the discipline is to master the methods and acquire theoretical and practical knowledge in the field of digital image processing. During the course, students will learn the basic algorithms of digital information processing, image restoration, image compression, and pattern recognition.

1. Gonzalez R., Woods R. Digital image processing. - M .: TECHNOSPHERE, 2006. – 1070 p. 2. Kirichenko NF, Matvienko VT Construction of multiple filters for linear algebraic systems. // Problems of management and informatics, №6, 2000, p.56-76. 3. Matvienko V.T., Cherniy D. I., Linder Y. M., Pichkur V. V An algorithm for finding similar objects in an image // Paper presented at the 2019 IEEE International Conference on Advanced Trends in Information Theory, ATIT 2019.

Lectures, laboratory work, independent work, elaboration of recommended literature, homework.

Semester assessment: The maximum number of points that can be obtained by a student is 60 points: Laboratory work №1: - 15/9 points. Laboratory work №2: - 15/9 points. Laboratory work №3: - 15/9 points. Laboratory work №4: - 15/9 points. Final assessment in the form of an exam.

Ukrainian