Title:

Computer Vision

Code:POV
Ac.Year:2016/2017
Term:Winter
Curriculums:
ProgrammeBranchYearDuty
IT-MSC-2MBI-Elective
IT-MSC-2MBS-Elective
IT-MSC-2MGM-Compulsory-Elective - group G
IT-MSC-2MIN-Compulsory-Elective - group I
IT-MSC-2MIS2ndElective
IT-MSC-2MMI-Elective
IT-MSC-2MMM-Elective
IT-MSC-2MPV2ndCompulsory-Elective - group G
IT-MSC-2MSK-Elective
Language:Czech
Credits:5
Completion:examination (written)
Type of
instruction:
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Hours:2600026
 ExaminationTestsExercisesLaboratoriesOther
Points:5190040
Guarantee:Zemčík Pavel, prof. Dr. Ing., DCGM
Lecturer:Beran Vítězslav, Ing., Ph.D., DCGM
Čadík Martin, doc. Ing., Ph.D., DCGM
Hradiš Michal, Ing., Ph.D., DCGM
Španěl Michal, Ing., Ph.D., DCGM
Zemčík Pavel, prof. Dr. Ing., DCGM
Instructor:Bartl Vojtěch, Ing., DCGM
Behúň Kamil, Ing., DCGM
Hradiš Michal, Ing., Ph.D., DCGM
Juránek Roman, Ing., Ph.D., DCGM
Pavelková Alena, Ing., DCGM
Sochor Jakub, Ing., DCGM
Špaňhel Jakub, Ing., DCGM
Zajíc Jiří, Ing., DCGM
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Graphics and Multimedia FIT BUT
 
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.
Description:
  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.
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.
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.
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
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
Progress assessment:
  Homeworks, Mid-term test, individual project.