#
FI:PV131 Digital Image Processing - Course Information

## PV131 Digital Image Processing

**Faculty of Informatics**

Spring 2022

**Extent and Intensity**- 2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).

Taught online. **Teacher(s)**- prof. RNDr. Michal Kozubek, Ph.D. (lecturer)

RNDr. Filip Lux (seminar tutor)

doc. RNDr. Martin Maška, Ph.D. (seminar tutor)

doc. RNDr. David Svoboda, Ph.D. (seminar tutor)

RNDr. David Wiesner (seminar tutor) **Guaranteed by**- prof. RNDr. Michal Kozubek, Ph.D.

Department of Visual Computing - Faculty of Informatics

Supplier department: Department of Visual Computing - Faculty of Informatics **Prerequisites**- Required knowledge: English, foundations of mathematics, linear algebra, calculus and basics of image processing at the level of PB130 course.
**Course Enrolment Limitations**- The course is also offered to the students of the fields other than those the course is directly associated with.
**fields of study / plans the course is directly associated with**- there are 76 fields of study the course is directly associated with, display
**Course objectives**- This course aims to broaden the knowledge of the basics of digital image processing gained in the PB130 course. The students will gain an overview of the available techniques and possibilities of this field. They will learn image transforms, segmentation algorithms and problems of object classification. They will be able to perform the basic techniques and apply them in practice. The lecture serves as the base for all those who want to attend to the topic in more detail.
**Learning outcomes**- The student will be able to:

- formulate basic principles of digital image processing;

- describe mutual relations between the analysis in spatial and frequency domain;

- realize basic workflows at least in MATLAB;

- suggest and apply suitable workflows for a given problem of image analysis; **Syllabus**- Acquisition of 2D and 3D image data, the process of signal digitization.
- Properties of digital images.
- Continuous convolution, PSF, OTF.
- Fourier transform and Nyquist sampling theorem.
- Image processing in the frequency domain.
- Non-linear filters.
- Multi-scale analysis, introduction to wavelet transform.
- Hough transform and Radon transform.
- Image segmentation.
- Image and object classification.
- Deep learning and convolutional neural networks in image analysis.

**Literature**- GONZALEZ, Rafael C. and Richard E. WOODS.
*Digital image processing*. 3rd ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2008. xxii, 954. ISBN 9780135052679. info - PRATT, William K.
*Digital image processing : PIKS scientific inside*. 4th ed. Hoboken, N.J.: Wiley-interscience, 2007. xix, 782. ISBN 9780471767770. info - SONKA, Milan, Václav HLAVÁČ and Roger BOYLE.
*Image processing analysis and machine vision [2nd ed.]*. 2nd ed. Pacific Grove: PWS Publishing, 1999. xxiv, 770. ISBN 0-534-95393-X. info

- GONZALEZ, Rafael C. and Richard E. WOODS.
**Teaching methods**- Lectures followed by class exercises in a computer room to gain hands-on experience.
**Assessment methods**- Lectures in Czech, study materials in English. Mandatory practicals (labs) on computers with compulsory homework. Written final exam, no materials allowed.
**Language of instruction**- Czech
**Follow-Up Courses****Further Comments**- The course is taught annually.

The course is taught: every week. **Listed among pre-requisites of other courses****Teacher's information**- http://cbia.fi.muni.cz/

- Enrolment Statistics (Spring 2022, recent)
- Permalink: https://is.muni.cz/course/fi/spring2022/PV131