Course detail

Computer Vision

FIT-POVaAcad. year: 2017/2018

Principles and methods of computer vision, methods and principles of image acquiring, preprocessing methods (statistical processing), filtering, pattern recognition, integral transformations - Fourier transform, image morphology, classification problems, automatic classification, D methods of computer vision, open problems of computer vision.

Learning outcomes of the course unit

The students will get acquainted with the principles and methods of computer vision. They will learn in more detail selected methods and algorithms of vision and image acquiring. They will also get acquainted with the possibilities of the scanned data processing. Finally, they will learn how to apply the gathered knowledge practically.

The students will improve their teamwork skills, mathematics, and exploitation of the "C" language.

Prerequisites

There are no prerequisites

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

  • Žára, J., kol.: Počítačová grafika-principy a algoritmy, Grada, 1992, ISBN 80-85623-00-5
  • Forsyth, D. A., Ponce, J.: Computer Vision A Modern Approach, Prentice Hall, New Jersey, USA, 2003, ISBN 0-13-085198-1

  • Horn, B.K.P.: Robot Vision, McGraw-Hill, 1988, ISBN 0-07-030349-5
  • Hlaváč, V., Šonka, M.: Počítačové vidění, Grada, 1993, ISBN 80-85424-67-3 
  • Russ, J.C.: The IMAGE PROCESSING Handbook, CRC Press, 1995, ISBN 0-8493-2532-3
  • Bass, M.: Handbook of Optics, McGraw-Hill, New York, USA, 1995, ISBN 0-07-047740-X

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.

Language of instruction

English

Work placements

Not applicable.

Course curriculum

    Syllabus of lectures:
    1. Introduction, basic principles, pre-processing and normalization (highlights)
    2. Segmentation, color analysis, histogram analysis, clustering
    3. Texture features analysis and acquiring
    4. Clusters, statistical methods
    5. Curves, curve parametrization
    6. Geometrical shapes extraction, Hough transform, RHT
    7. Pattern recognition (statistical, structural)
    8. Classifiers (AdaBoost, neural nets...), automatic clustering
    9. Detection and parametrization of objects in images
    10. Geometrical transformations, RANSAC applications
    11. Motion analysis, object tracking
    12. 3D methods of computer vision, registration, reconstruction
    13. Conclusion, open problems of computer vision

    Syllabus - others, projects and individual work of students:
    1. Homeworks (5 runs) at the beginning of semester
    2. Individually assigned project for the whole duration of the course.

Aims

To get acquainted with the principles and methods of computer vision. To learn in more detail selected methods and algorithms of vision and image acquiring. To get acquainted with the possibilities of the scanned data processing. To learn how to apply the gathered knowledge practically.

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

Homeworks, Mid-term test, individual project.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MBI , any year of study, winter semester, 5 credits, elective
    branch MPV , any year of study, winter semester, 5 credits, compulsory-optional
    branch MGM , any year of study, winter semester, 5 credits, compulsory-optional
    branch MSK , any year of study, winter semester, 5 credits, elective
    branch MBS , any year of study, winter semester, 5 credits, elective
    branch MIN , any year of study, winter semester, 5 credits, compulsory-optional
    branch MMM , any year of study, winter semester, 5 credits, elective

  • Programme IT-MGR-1H Master's

    branch MGH , any year of study, winter semester, 5 credits, recommended

  • Programme IT-MGR-2 Master's

    branch MIS , 2. year of study, winter semester, 5 credits, elective