Digital Image Processing

Garant předmětu:

Lecturer:

Tutor:

Supervisor:

Department of Mathematics

Synopsis:

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, Wiener filtering, blind deconvolution), edge detection, morphology and geometric transformations and warping. Numerous applications and experimental results are presented in addition to the theory.

Requirements:

Syllabus of lectures:

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 feature detection

image degradations and their modelling, inverse and Wiener filtering, restoration of motion-blurred and out-of-focus blurred images

image segmentation

mathematical morphology

image registration and matching

Syllabus of tutorials:

Study Objective:

Study materials:

Key references:

[1] Gonzales R. C., Woods R. E.: Digital Image Processing (4th ed.), Pearson, 2018

[2] Pratt W. K.: Introduction to Digital Image Processing, CRC Press, 2013

Recommended references:

[3] J. Flusser, T. Suk, B. Zitova: 2D and 3D Image Analysis by Moments 1st Edition, Wiley & Sons Ltd., (2016)

[4] J. Flusser, T. Suk, B. Zitová: Moments and Moment Invariants in Pattern Recognition, Wiley & Sons Ltd., (2009)

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: