Course detail
Computer Vision
FEKT-MPOVAcad. year: 2010/2011
See "Curriculum".
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
2. Image preprocessing
3. Integral transform I.
4. Integral transform II.
5. Image segmentation
6. Region-based segmentation and clustering
7. Description and shape reprezentation
8. Mathematical morphology
9. Classification and automatic sorting
10. Local features and correspondence
11. Image understanding
12. Motion analysis I.
13. Motion analysis II.
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision. Thomson 2008. ISBN 978-0-495-08252-1. (EN)
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
Basic principles of computer vision
Methods and principles of image acquiring
Representations of image data and their features
Image preprocessing, statistical image processing
Integral image transforms - Fourier transform
Features of Fourier transform, fast Fourier transform
Wavelet transform
Discrete cosine transform, L-V transform
Image morphology
Classification problems, automatic classification
3D methods of computer vision
Conclusion, open problems of computer vision
Laboratory exercise
Teacher / Lecturer
Syllabus