Title:

Image Processing

Code:ZPO
Ac.Year:2019/2020
Sem:Summer
Curriculums:
ProgrammeField/
Specialization
YearDuty
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
MITAINADE-Elective
MITAINBIO-Elective
MITAINCPS-Elective
MITAINEMB-Elective
MITAINGRI-Elective
MITAINHPC-Elective
MITAINIDE-Elective
MITAINISD-Elective
MITAINISY-Elective
MITAINMAL-Elective
MITAINMAT-Elective
MITAINNET-Elective
MITAINSEC-Elective
MITAINSEN-Elective
MITAINSPE-Elective
MITAINVER-Elective
MITAINVIZ-Compulsory
Language of Instruction:Czech
Credits:5
Completion:examination (written)
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:2600026
 ExamsTestsExercisesLaboratoriesOther
Points:51100039
Guarantor:Zemčík Pavel, prof. Dr. Ing. (DCGM)
Deputy guarantor:Beran Vítězslav, Ing., Ph.D. (DCGM)
Lecturer:©paněl 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.Groups
FrilecturelecturesE112 12:0013:501MIT 2MIT MGM MMI xx
 
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 competencies:
  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 competencies:
  Students will improve their teamwork skills and in exploitation of "C" language.
Syllabus of lectures:
 
  1. Introduction, representation of image
  2. Linear filtration
  3. Image acquisition
  4. Discrete image transforms, FFT, relationship with filtering
  5. Point image transforms
  6. Edge detection, segmentation
  7. Resampling, warping, morphing
  8. DCT, Wavelets
  9. Watermarks
  10. Image distortion, types of noise
  11. Optimal filtration
  12. Mathematical Morphology
  13. Motion analysis, conclusion
Syllabus - others, projects and individual work of students:
 
  1. Individually assigned project for the whole duration of the course.
Fundamental literature:
 
  • Hlaváč, V., ©onka, M.: Počítačové vidění, GRADA 1992, ISBN 80-85424-67-3
  • ©onka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN-13: 978-9386858146
  • IEEE Multimedia, IEEE, USA - série časopisů - různé články
  • 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 
  • ©onka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN-13: 978-9386858146
  • Jahne, B.: Handbook of Computer Vision and Applications, Academic Press, 1999, ISBN 0-12-379770-5
Progress assessment:
  Mid-term test, project (homeworks and individual project).
 

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