PB130 Introduction to Digital Image Processing

Faculty of Informatics
Spring 2014
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
doc. RNDr. Petr Matula, Ph.D. (lecturer)
RNDr. Martin Maška, Ph.D. (seminar tutor)
Dmitry Sorokin, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing - Faculty of Informatics
Supplier department: Department of Visual Computing - Faculty of Informatics
Mon 8:00–9:50 D1
  • Timetable of Seminar Groups:
PB130/01: Wed 11:00–11:50 B311, P. Matula
PB130/02: Fri 8:00–8:50 B311, M. Maška
PB130/03: Fri 9:00–9:50 B311, M. Maška
PB130/04: Wed 10:00–10:50 B311, M. Maška
! PV131 Digital Image Processing
Knowledge of high-school mathematics and understanding of simple algorithms is supposed.
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 37 fields of study the course is directly associated with, display
Course objectives
At the end of the course a student should: know the basic terminology related to digital image processing; know about the typical problems from digital image processing; understand the principle of simple algorithms for image processing and know how to use them. The course is intended as an introduction to digital image processing.
  • Human vision, image acquisition and basic image computer representations.
  • Color images.
  • Point transforms. Histogram.
  • Linear image filtering. Convolution.
  • Non-linear filters.
  • Mathematical morphology.
  • Edge detection. Gradient.
  • Regions in binary images and their description.
  • Image segmentation.
  • Applications of digital image processing.
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing. 2nd ed. Upper Saddle River: Prentice Hall, 2002. xx, 793. ISBN 0130946508. 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 mandatory homeworks. Written final exam, no materials allowed.
Language of instruction
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2019, Autumn 2020, Autumn 2021.
  • Enrolment Statistics (Spring 2014, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2014/PB130