Course detail

Image Analysis in Material Science

FSI-WONAcad. year: 2025/2026

The aim of the course is to provide students with fundamental information about image
processing for technical purposes. The course deals with colour spaces and methods of
computer image modelling, brightness and kontrast modification, linear and non-linear image filters and its application, objects recognition and analysis.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Pavel Štarha, Ph.D.

Department

Institute of Mathematics (ÚM)

Entry knowledge

Secondary school mathematics and informatics

Rules for evaluation and completion of the course

Submitted a semester work, written and oral exam
Missed lessons can be compensated for via make-up topics of exercises.

Aims

The aim of the course is to provide students with information about current computer image processing methods for technical purposes.
Basic knowledge of present image processing and its use in practice.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Hlaváč, V., Šonka, M.: Počítačové vidění, Grada, 1993
Druckmüller, M., Heriban, P.: Digital Image Processing System for Windows, ver. 5.0., SOFO Brno, 1996
Martišek, D.: Počítačová geometrie a grafika, Brno 2017. (CS)

Recommended reading

Pratt, W. K. Digital Image Processing (Third Edition) PIKS Inside [online]. 3rd ed. New York: Wiley-Interscience, 2001 [cit. 2014-08-07]. ISBN 04-712-2132-5. (EN)

Classification of course in study plans

  • Programme B-ZSI-P Bachelor's

    specialization MTI , 1 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Pavel Štarha, Ph.D.

Syllabus

1. Image representation, basic graphics formats.
2. Colour spaces, pixel properties.
3. Basic operation with images - geometric transformations, logical operations.
4. Basics of probability and statistics.
5. Histogram and its basic meaning, brightness linear transformation.
5. Histogram and its basic meaning, linear brightness transformations.
6. Non-linear brightness transformation, histogram equalization.
7. Fourier transform and its applications.
8. Convolution, convolution theorem.
9. Linear and non-linear filters.
10. Additive noise - analysis and filtering.
11. Impulse noise - analysis and filtering.
12. Image segmentation, object analysis.
13. Moment method of object analysis.

Exercise

14 hod., compulsory

Teacher / Lecturer

doc. Ing. Pavel Štarha, Ph.D.

Syllabus

Week 1: Familiarization with the tutorials.
Week 3: Basic image operations, brightness and contrast adjustment. Application of linear filters.
Week 6: Fourier spectrum of an image.
Week 7: Relationship between Fourier transform and convolution.
Week 9: Application of nonlinear filters.
Week 13: Moment method of object recognition, measurement protocol, results presentation.

Presence in the seminar is obligatory.

Computer-assisted exercise

12 hod., compulsory

Teacher / Lecturer

doc. Ing. Pavel Štarha, Ph.D.

Syllabus

Week 2: Work with different graphic formats.
Week 4: Image histogram and pixel value non-linear transformation.
Week 5: Histogram equalization.
Week 10: Working with additive noise.
Week 11: Working with impulse noise.
Week 12: Thresholding, object analysis, basic statistics.

Presence in the seminar is obligatory.