Course detail
Image Processing
FIT-ZPOAcad. year: 2020/2021
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
Supervisor
Learning outcomes of the course unit
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.
Students will improve their teamwork skills and in exploitation of "C" language.
Prerequisites
The C programming language and fundamentals of computer graphics.
- recommended prerequisite
Co-requisites
Not applicable.
Recommended optional programme components
Not applicable.
Recommended or required reading
Hlaváč, V., Šonka, M.: Počítačové vidění, GRADA 1992, ISBN 80-85424-67-3
Šonka, 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
Planned learning activities and teaching methods
Not applicable.
Assesment methods and criteria linked to learning outcomes
Mid-term test, project (homeworks and individual project).
Language of instruction
Czech, English
Work placements
Not applicable.
Aims
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.
Classification of course in study plans
- Programme IT-MGR-2 Master's
branch MBI , any year of study, summer semester, 5 credits, elective
branch MPV , any year of study, summer semester, 5 credits, compulsory-optional
branch MSK , any year of study, summer semester, 5 credits, elective
branch MIS , any year of study, summer semester, 5 credits, elective
branch MBS , any year of study, summer semester, 5 credits, elective
branch MIN , any year of study, summer semester, 5 credits, elective
branch MMM , any year of study, summer semester, 5 credits, elective - Programme MITAI Master's
specialization NADE , any year of study, summer semester, 5 credits, elective
specialization NBIO , any year of study, summer semester, 5 credits, elective
specialization NGRI , any year of study, summer semester, 5 credits, elective
specialization NNET , any year of study, summer semester, 5 credits, elective
specialization NVIZ , any year of study, summer semester, 5 credits, compulsory
specialization NCPS , any year of study, summer semester, 5 credits, elective
specialization NSEC , any year of study, summer semester, 5 credits, elective
specialization NEMB , any year of study, summer semester, 5 credits, elective
specialization NHPC , any year of study, summer semester, 5 credits, elective
specialization NISD , any year of study, summer semester, 5 credits, elective
specialization NIDE , any year of study, summer semester, 5 credits, elective
specialization NISY , any year of study, summer semester, 5 credits, elective
specialization NMAL , any year of study, summer semester, 5 credits, elective
specialization NMAT , any year of study, summer semester, 5 credits, elective
specialization NSEN , any year of study, summer semester, 5 credits, elective
specialization NVER , any year of study, summer semester, 5 credits, elective
specialization NSPE , any year of study, summer semester, 5 credits, elective - Programme IT-MGR-2 Master's
branch MGM , 1. year of study, summer semester, 5 credits, compulsory
branch MMI , 1. year of study, summer semester, 5 credits, compulsory
Type of course unit
Lecture
26 hours, optionally
Teacher / Lecturer
Syllabus
- Introduction, representation of image
- Linear filtration
- Image acquisition
- Discrete image transforms, FFT, relationship with filtering
- Point image transforms
- Edge detection, segmentation
- Resampling, warping, morphing
- DCT, Wavelets
- Watermarks
- Image distortion, types of noise
- Optimal filtration
- Mathematical Morphology
- Motion analysis, conclusion
Project
26 hours, compulsory
Teacher / Lecturer
Syllabus
- Individually assigned project for the whole duration of the course.