SubjectsSubjects(version: 949)
Course, academic year 2021/2022
Image Processing - AP445002
Title: Image Processing
Guaranteed by: Department of Mathematics, Informatics and Cybernetics (446)
Faculty: Faculty of Chemical Engineering
Actual: from 2021
Semester: both
Points: 0
E-Credits: 0
Examination process:
Hours per week, examination: 3/0, other [HT]
Capacity: winter:unknown / unknown (unknown)
summer:unknown / unknown (unknown)
Min. number of students: unlimited
Language: English
Teaching methods: full-time
Teaching methods: full-time
For type: doctoral
Note: course is intended for doctoral students only
can be fulfilled in the future
you can enroll for the course in winter and in summer semester
Guarantor: Mareš Jan doc. Ing. Ph.D.
Interchangeability : P445002
Annotation -
Last update: Mudrová Martina Ing. Ph.D. (29.08.2018)
The subject presents principles of image processing and functional transforms usage in image analysis. Special attention is paid to colour processing, image segmentation, noise components rejection, image enhancement, data compression, pattern recognition and feature classification including estimation of accuracy and cross-validation errors. Applications include processing of images related to chemistry, material engineering, biochemistry, and biomedicine.
Aim of the course -
Last update: Pátková Vlasta (08.06.2018)

Student will know how:

  • to choose appropriate methods for image processing
  • to apply selected mathematical methods and computational tools for image analysis
  • to use pattern recognition and classification methods in multidimensional data processing

Literature -
Last update: Mudrová Martina Ing. Ph.D. (17.09.2018)

R: Gonzales R., Woods R., Digital Image Processing, Prentice Hall, 2008

A: I.T. Jolliffe: Principal Component Analysis, 2nd ed., Springer-Verlag, New York, 2002

Learning resources -
Last update: Pátková Vlasta (08.06.2018)

Teaching methods -
Last update: Pátková Vlasta (08.06.2018)

Lectures, project focussed on image processing metohod use

Requirements to the exam -
Last update: Pátková Vlasta (08.06.2018)


Syllabus -
Last update: Pátková Vlasta (08.06.2018)

Subject is focussed primarily on

1. Fundamentals of computational, programming and visualization system MATLAB / SIMULINK

2. Mathematical representation of multidimensional signals, image coding

3. Selected numerical methods for image processing: interpolation, approximation

4. Two-dimensional discrete Fourier transform in image analysis and resolution changes

5. Multi-dimensional digital filters in image-denoising

6. Gradient methods in image enhancement

7. Selected methods of image segmentation, feature extraction and classification

8. Discrete wavelet transform in data compression image denoising

9. Computational intelligence in microscopic data analysis and classification

10. Spatial data visualization and processing

Entry requirements -
Last update: Pátková Vlasta (08.06.2018)


Registration requirements -
Last update: Pátková Vlasta (08.06.2018)


Course completion requirements -
Last update: Pátková Vlasta (08.06.2018)

Evaluation is based upon the individual project and oral exam