PV131 Digital Image Processing

Faculty of Informatics
Autumn 2018
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Alternate Types of Completion: z (credit).
Teacher(s)
prof. RNDr. Michal Kozubek, Ph.D. (lecturer)
RNDr. Martin Maška, Ph.D. (seminar tutor)
doc. RNDr. David Svoboda, Ph.D. (seminar tutor)
Supervisor
doc. RNDr. Petr Matula, Ph.D.
Department of Computer Graphics and Design - Faculty of Informatics
Supplier department: Department of Computer Graphics and Design - 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 the course is directly associated with
there are 38 fields of study the course is directly associated with, display
Course objectives
This course aims to broaden the knowledge of 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 and registration 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 details.
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.
  • Fourier transform and Nyquist sampling theorem.
  • Convolution, PSF, OTF.
  • Image processing in the frequency domain, non-linear filters.
  • Multi-scale analysis, introduction to wavelet transform.
  • Edge detection.
  • Hough transform and Radon transform.
  • Image segmentation.
  • Object classification.
  • Image registration.
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
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/
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.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2018/PV131

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