Title:

Image Processing

Code:ZPO
Ac.Year:2017/2018
Term:Summer
Curriculums:
ProgrammeBranchYearDuty
IT-MSC-2MBI-Elective
IT-MSC-2MBS-Elective
IT-MSC-2MGM1stCompulsory
IT-MSC-2MIN-Elective
IT-MSC-2MIS-Elective
IT-MSC-2MMI1stCompulsory
IT-MSC-2MMM-Elective
IT-MSC-2MPV-Compulsory-Elective - group M
IT-MSC-2MSK-Elective
Language:Czech
Credits:5
Completion:examination (written)
Type of
instruction:
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Hours:2600026
 ExaminationTestsExercisesLaboratoriesOther
Points:51100039
Guarantee:Zemčík Pavel, prof. Dr. Ing., DCGM
Lecturer:Španěl Michal, Ing., Ph.D., DCGM
Zemčík Pavel, prof. Dr. Ing., DCGM
Instructor:Beran Vítězslav, Ing., Ph.D., DCGM
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Graphics and Multimedia FIT BUT
Prerequisites: 
Computer Graphics (PGR), DCGM
Follow-ups:
Computer Vision (POVa), DCGM
Schedule:
DayLessonWeekRoomStartEndLect.Gr.St.G.EndG.
FrilecturelecturesE11212:0013:501MIT12 MGM12 MGM
FrilecturelecturesE11212:0013:501MIT15 MMI15 MMI
FrilecturelecturesE11212:0013:502MITxxxx
 
Learning objectives:
  To get acquainted with the image processing basics theory (transformations, filtration, noise reduction, etc.). To learn how to apply such knowledge on real examples of image processing tasks. To get acquainted with "higher" imaging algorithms. To learn kow to practically program image processing applications through projects.
Description:
  Introduction to image processing, image acquiring, point and discrete image transforms, linear image filtering, image distortions, types of noise, optimal image filtering, non-linear image filtering, watermarks, edge detection, segmentation, motion analysis, loseless and lossy image compression
Knowledge and skills required for the course:
  The C programming language and fundamentals of computer graphics.
Subject specific learning outcomes and competences:
  The students will get acquainted with the image processing basics theory (transformations, filtration, noise reduction, etc.). They will learn how to apply such knowledge on real examples of image processing tasks. They will also get acquainted with "higher" imaging algorithms. Finally, they will learn how to practically program image processing applications through projects.
Generic learning outcomes and competences:
  Students will improve their teamwork skills and in exploitation of "C" language.
Syllabus of lectures:
 
  1. Introduction to image processing
  2. Image data acquiring
  3. Point image transforms
  4. Discrete image transforms
  5. Linear image filtering
  6. Image distortion, types of noise
  7. Optimal filtering
  8. Nonlinear image filtering
  9. Watermarks
  10. Edge detection, segmentation
  11. Movement analysis
  12. Image compression, lossy, looseless
  13. Future of image processing
Syllabus - others, projects and individual work of students:
 
  1. Individually assigned project for the whole duration of the course.
Fundamental literature:
 
  • Hlaváč, V., Šonka, M.: Počítačové vidění, GRADA 1992, ISBN 80-85424-67-3
  • Jahne, B.: Handbook of Computer Vision and Applications, Academic Press, 1999, ISBN 0-12-379770-5
  • Russ, J.C.: The Image Processing Handbook, CRC Press 1995, ISBM 0-8493-2516-1
Study literature:
 
  • Russ, J.C.: The Image Processing Handbook, CRC Press 1995, ISBM 0-8493-2516-1
Progress assessment:
  Mid-term test, individual project.