PV131 Digital Image Processing

Faculty of Informatics
Spring 2020
Extent and Intensity
2/2. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
prof. RNDr. Michal Kozubek, Ph.D. (lecturer)
doc. RNDr. Martin Maška, Ph.D. (seminar tutor)
doc. RNDr. David Svoboda, Ph.D. (seminar tutor)
RNDr. David Wiesner, Ph.D. (assistant)
Guaranteed by
prof. RNDr. Michal Kozubek, Ph.D.
Department of Visual Computing – Faculty of Informatics
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable
Mon 17. 2. to Fri 15. 5. Tue 10:00–11:50 B410; and Tue 19. 5. 8:00–9:50 B410
  • Timetable of Seminar Groups:
PV131/01: Mon 17. 2. to Fri 15. 5. Thu 8:00–9:50 B311; and Tue 19. 5. 10:00–11:50 B311, D. Svoboda
PV131/02: Mon 17. 2. to Fri 15. 5. Thu 14:00–15:50 B311; and Tue 19. 5. 14:00–15:50 B311, M. Maška
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 75 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
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
Study Materials
The course is taught annually.
Teacher's information
http://cbia.fi.muni.cz/
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Spring 2021, Spring 2022, Spring 2023.
  • Enrolment Statistics (Spring 2020, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2020/PV131