Digital Image Processing - NPGR002
Title: Digitální zpracování obrazu
Guaranteed by: Department of Software and Computer Science Education (32-KSVI)
Faculty: Faculty of Mathematics and Physics
Actual: from 2022
Semester: winter
E-Credits: 4
Hours per week, examination: winter s.:3/0, Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Additional information: http://zoi.utia.cas.cz/teaching
Guarantor: prof. Ing. Jan Flusser, DrSc.
Class: Informatika Bc.
Informatika Mgr. - volitelný
M Bc. MMIT
M Bc. MMIT > Povinně volitelné
M Mgr. MMIB
M Mgr. MMIB > Povinně volitelné
Classification: Informatics > Computer Graphics and Geometry
Is co-requisite for: NPGR029, NPGR013, NMPG349
Is incompatible with: NPGX002
Is interchangeable with: NPGX002
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Annotation -
Last update: T_KSVI (24.04.2003)
An introductory course on image processing and pattern recognition. Major attention is paid to image sampling and quantization, image preprocessing (noise removal, contrast stretching, sharpening, and de-blurring), edge detection, geometric transformations and warping, features for shape description and recognition, and to general pattern recognition techniques. Numerous applications and experimental results are presented in addition to the theory.
Course completion requirements -
Last update: prof. Ing. Jan Flusser, DrSc. (28.10.2019)

"Face to face" oral exam, no programming.
Literature -
Last update: prof. Ing. Jan Flusser, DrSc. (28.10.2019)

[1] Pratt W. K.: Digital Image Processing (2nd ed.), John Wiley, New York, 1991

[2] Rosenfeld A., Kak A. C.: Digital Picture Processing, Academic Press, New York, 1982

[3] Gonzales R. C., Woods R. E., Digital Image Processing (3rd ed.), Addison-Wesley, 1992

[4] Duda R.O. et al., Pattern Classification, (2nd ed.), John Wiley, New York, 2001
Syllabus -
Last update: T_KSVI (24.04.2003)
  • image sampling and quantization, Shannon theorem, aliasing
  • basic image operations, histogram, contrast stretching, noise removal, image sharpening
  • linear filtering in the spatial and frequency domains, convolution, Fourier transform
  • edge detection, corner detection
  • image degradations and their modelling, inverse and Wiener filtering, restoration of motion-blurred and out-of-focus blurred images
  • image segmentation
  • image registration and matching
  • features for description and recognition of 2-D shapes
  • invariant features, Fourier descriptors, moment invariants, differential invariants
  • statistical pattern recognition, supervised and nonsupervised classification, NN- classifier, linear classifier, Bayessian classifier
  • clustering in a feature space, iterative and hierarchical methods
  • dimensionality reduction of a feature space