Image processing and recognition

Course: Applied Mathematics

Structural unit: Faculty of Computer Science and Cybernetics

Title
Image processing and recognition
Code
ДВС.3.06.02.02
Module type
Вибіркова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
8 Semester
Number of ECTS credits allocated
3
Learning outcomes
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, finding optimal solutions. PLO 26.2 Be able to implement automatic and automated systems that implement built mathematical and computer models, developed algorithms.
Form of study
Full-time form
Prerequisites and co-requisites
To successfully study the discipline "Image Processing and Recognition" the student must meet the following requirements: 1. Know: 1) Fundamentals of mathematical analysis, linear algebra, discrete mathematics, differential equations, operations research, numerical methods. 2) Image processing software. 2. Be able to: 1) Apply basic algorithms for filtering, restoration, recognition in information processing. 2) Apply algorithms for digital information processing. 3. Possess: 1) Skills of construction, analysis, and application of mathematical methods in solving problems of image processing.
Course content
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.
Recommended or required reading and other learning resources/tools
1. Gonzalez R., Woods R. Digital image processing. - M .: TECHNOSPHERE, 2006. –1070 p. 2. Kirichenko N.F., Matvienko V.T. 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.
Planned learning activities and teaching methods
Lectures, laboratory work, independent work, elaboration of recommended literature, homework.
Assessment methods and criteria
Semester assessment: A maximum number of points that can be obtained by a student: 100 points: Laboratory work №1: - 50/30 points. Laboratory work №2: - 50/30 points. Defense of laboratory work 1 - 2 involves the delivery of a laboratory project and answers to theoretical questions on its topic.
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Volodymyr T. Matvienko
Complex systems modelling
Faculty of Computer Science and Cybernetics

Departments

The following departments are involved in teaching the above discipline

Complex systems modelling
Faculty of Computer Science and Cybernetics