Image processing and recognition 1

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:

Passing linear algebra and calculus

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

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:

Image visualisation and Matlab basics

Fourier transform

Noise and denoising methods

Edge detectors and histogram equalisation

Image registration

Morphology

Study Objective:

to teach students introduction to image processing

Study materials:

Key references:

Gonzales R. C., Woods R. E., Digital Image Processing (3rd ed.), Addison-Wesley, 2008

ecommended references:

Pratt W. K.: Digital Image Processing (3rd ed.), John Wiley, New York, 2001

Media and tools: a complete set of lecture slides and excersises are available at http://zoi.utia.cas.cz/ROZ1

Note:

Time-table for winter semester 2011/2012:

Time-table is not available yet

Time-table for summer semester 2011/2012:

Time-table is not available yet

The course is a part of the following study plans: