Course detail

Computer Vision Applications

FEKT-NAPVAcad. year: 2019/2020

Not applicable.

Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

Ing. Karel Horák, Ph.D.

Department

Department of Control and Instrumentation (UAMT)

Learning outcomes of the course unit

Not applicable.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Course curriculum

1. Special application in computer vision.
2. Cluster-based segmentation.
3. Local features and correspondences.
4. Region detector.
5. Region descriptors.
6. Global and combined descriptors.
7. Image understanding.
8. Distance and risk minimization classification.
9. Dynamic images.
10. Multiimage reconstruction.
11. Learning in recognition.
12. Selected passages of recognition.

Work placements

Not applicable.

Aims

Not applicable.

Specification of controlled education, way of implementation and compensation for absences

Not applicable.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Basic literature

Not applicable.

Recommended reading

Jahne B., Hausecker H., Geisler P.: Handbook of Computer Vision and Applications. Academic press 1999. ISBN 0-12-379770-5. (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

  • Programme EECC-MN Master's

    branch MN-KAM , 1 year of study, summer semester, elective specialised

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Karel Horák, Ph.D.

Syllabus

1. Implementation of computer vision in technical practice – introduction, motivation, advantages and drawbacks, typical problems of camera systems applications, methodology of order process
2. Basic physical principles used in computer vision
3. Particularity of hardware for image acquisition and processing
4. Measuring in plain – precise measuring of dimensions, position and orientation
5. Detection of product presence and completeness, counting of objects in image, classification according to shape, colour, surface attributes etc.
6. Defectoscopy, inspection systems – detection of product surface defects, inspection of transparent materials etc.
7. OCR – licence plates, character reading, conversion of printed book to electronic
8. Measuring of 3D dimensions, volume metering, 3D digital models
9. Area navigation, robot positioning – 3D, trajectory monitoring
10. Motion – motion detection, moving objects detection, trajectory monitoring, 3D attributes of objects. Traffic problems – velocity measuring, red-light crossing vehicles detection, critical states detection
11. Biological images analysis, biometric data measuring
12. Other applications – contactless temperature metering (thermocamera), deformation metering (interferometer), astral sky image analysis
13. Computer vision together with computer graphics

Laboratory exercise

26 hod., compulsory

Teacher / Lecturer

Ing. Karel Horák, Ph.D.

Syllabus

Individually assigned project for the whole duration of the course. Projects solved partial problems connected with research activities of Group of computer vision UAMT. Thematic domains:
- dimensions measuring
- detection and recognitions of surface defects on electronic components
- recognition of objects in image
- 3D problems
- traffic situations monitoring
- and others