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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
Additional information:	http://zoi.utia.cas.cz/teaching

Guarantor:	prof. Ing. Jan Flusser, DrSc.
Teacher(s):	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

Opinion survey results Examination dates WS schedule Noticeboard

Annotation -

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.

Last update: T_KSVI (24.04.2003)

Course completion requirements -

"Face to face" oral exam, no programming.

Last update: Flusser Jan, prof. Ing., DrSc. (28.10.2019)

Literature -

[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

Last update: Flusser Jan, prof. Ing., DrSc. (28.10.2019)

Syllabus -

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

Last update: T_KSVI (24.04.2003)