Course details

Computer Vision

POV Acad. year 2007/2008 Winter semester 5 credits

Current academic year

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

Guarantor

Language of instruction

Czech

Completion

Examination (written)

Time span

26 hrs lectures, 26 hrs projects

Assessment points

50 exam, 10 half-term test, 40 projects

Department

Lecturer

Instructor

Subject specific learning outcomes and competences

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.

Generic learning outcomes and competences

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

Learning objectives

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.

Study literature

  • 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

Fundamental literature

  • 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

Syllabus of lectures

  1. Introduction, basic principles, pre-processing and normalization
  2. Segmentation, colour 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. Individually assigned project for the whole duration of the course.

Progress assessment

Mid-term test, individual project.

Course inclusion in study plans

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