Course detail
Computer Vision
FEKT-MPOVAcad. year: 2012/2013
See "Curriculum".
Language of instruction
Czech
Number of ECTS credits
6
Mode of study
Not applicable.
Guarantor
Learning outcomes of the course unit
Knowledge in computer vision theory.
Prerequisites
The subject knowledge on the Bachelor´s degree level is requested.
Co-requisites
Not applicable.
Planned learning activities and teaching methods
Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.
Assesment methods and criteria linked to learning outcomes
Requirements for completion of the course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.
Course curriculum
1. Introduction to computer vision
2. Image preprocessing
3. Integral transform I.
4. Integral transform II.
5. Image segmentation
6. Region-based segmentation and clustering
7. Description
8. Mathematical morphology
9. Classification and automatic sorting
10. Local features and correspondences
11. Image understanding
12. Motion analysis
2. Image preprocessing
3. Integral transform I.
4. Integral transform II.
5. Image segmentation
6. Region-based segmentation and clustering
7. Description
8. Mathematical morphology
9. Classification and automatic sorting
10. Local features and correspondences
11. Image understanding
12. Motion analysis
Work placements
Not applicable.
Aims
Not applicable.
Specification of controlled education, way of implementation and compensation for absences
The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.
Recommended optional programme components
Not applicable.
Prerequisites and corequisites
Not applicable.
Basic literature
Not applicable.
Recommended reading
Russ J.C.: The Image Processing Handbook. CRC Press 1995. ISBN 0-8493-2516-1. (EN)
Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision. Thomson 2008. ISBN 978-0-495-08252-1. (EN)
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
39 hod., optionally
Teacher / Lecturer
Syllabus
Introduction, applications fo computer vision
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
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
26 hod., compulsory
Teacher / Lecturer
Syllabus
Individually assigned project for the whole duration of the course