Course detail
Image Analysis in Material Science
FSI-WON Acad. year: 2024/2025 Winter semester
Supervisor
Department
Learning outcomes of the course unit
Prerequisites
Secondary school mathematics and informatics
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Language of instruction
Czech
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.
Specification of controlled education, way of implementation and compensation for absences
The study programmes with the given course
Programme C-AKR-P: , Lifelong learning
branch CZS: , 5 credits, elective
Programme B-ZSI-P: Fundamentals of Mechanical Engineering, Bachelor's
branch MTI: Materials Engineering, 5 credits, compulsory
Type of course unit
Lecture
26 hours, optionally
Teacher / Lecturer
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 hours, compulsory
Teacher / Lecturer
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 hours, compulsory
Teacher / Lecturer
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.