The aim of the subject is to provide knowledge in the field of methods of processing real images into digital form, improving the quality of digital images and practical implementation of digital image processing and analysis.
Wymagania wstępne
Linear algebra, Computer programming.
Zakres tematyczny
Lecture
Introduction to digital image processing (examples of applications, basic processing stages, elements of a digital image processing system).
Basics of digital image processing (perception, light and electromagnetic spectrum, image acquisition and sensors, sampling and quantization, mathematical tools of digital image processing).
Digital image processing methods (geometric transformations, point transformations, context-free image filtering, context-sensitive image filtering).
Filtering in the frequency domain (frequency transform, Fourier transform, discrete Fourier transform, DFT computational complexity, fast FFT transform, image transform, image filtering).
Morphological transformations (erosion and dilation, opening and closing, selected morphological algorithms).
Segmentation (detection of points, lines and edges, thresholding, area expansion, division and merging of areas, detection of moving objects).
Project
Developing projects according to the instructions that students will receive at the beginning of the semester. By implementing assigned projects, students learn the practical implementation of digital image processing methods.
Metody kształcenia
Traditional lecture. Design classes. Developing projects according to the instructions that students will receive at the beginning of the semester.
Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się
Opis efektu
Symbole efektów
Metody weryfikacji
Forma zajęć
Warunki zaliczenia
Checking the degree of students' preparation and their activity during project classes.
Assessment of knowledge and skills related to the implementation of project tasks, assessment of the project implementation report.
A written test consisting of questions and tasks verifying knowledge of the material covered.
The grade for the course consists of the grade for project classes (40%) and the grade for the lecture (60%). The condition for passing the course is a positive grade for the project and lecture.
Literatura podstawowa
Rafael C. Gonzalez, Richard E. Woods Digital Image Processing, Global Edition PEARSON Education Limited, 2017
Wilhelm Burger Principles of Digital Image Processing Springer London Ltd, 2009
Literatura uzupełniająca
Digital Image Processing [@] https://www.tutorialspoint.com/dip/index.htm
Uwagi
Zmodyfikowane przez dr Ewa Sylwestrzak-Maślanka (ostatnia modyfikacja: 06-05-2024 20:36)
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