Course detail
Advanced Techniques of Image Processing
FEKT-MKC-PZOAcad. year: 2023/2024
The course is focused on the theory and practice of advanced image processing techniques. Video sequence processing is included. The main areas of interest are camera model, its calibration, DFT image filtering, convolution, object recognition, biometric feature recognition (skin, face, papilar lines), epipolar geometry, stereo pair analysis, correspondence problem, spatial information extraction, dynamic programming, homography, optical flow, motion in scene tracking. The emphasis is on practical aspects of image processing, i.e. algorithmizing and implementing generally formulated tasks.
Language of instruction
Czech
Number of ECTS credits
6
Mode of study
Not applicable.
Guarantor
Department
Entry knowledge
Students who will sign up for the course should be able to:
describe the basic steps of signal digitalization,
interpret the basic principle of one-dimensional function derivative,
master the basic operators and functions of the C++ language,
describe the basic operations of matrix calculus,
describe basic principles of Euclidean geometry,
practice calculations with complex numbers,
discuss the basic terms from the domain of probability and statistics.
Generally, knowledge on the level of completed bachelor studies is expected.
describe the basic steps of signal digitalization,
interpret the basic principle of one-dimensional function derivative,
master the basic operators and functions of the C++ language,
describe the basic operations of matrix calculus,
describe basic principles of Euclidean geometry,
practice calculations with complex numbers,
discuss the basic terms from the domain of probability and statistics.
Generally, knowledge on the level of completed bachelor studies is expected.
Rules for evaluation and completion of the course
- up to 60 points for the written exam
- up to 40 points for laboratory tests
The written exam is taken at the end of semester. It contains simple tasks in the form of concrete questions as to mathematical constructions, constraints of algorithms, basic equations and principles of methods, often in the form of a fill-in quiz.
Two tests are arranged in the laboratory exercises (in the 6th and 12th weeks of the semester) each for max. 20 points. The tests contain tasks corresponding to the content of realised laboratory exercises. There is no lab-test-based qualification limit for the written exam.
Computer exercises are obligatory. Justified absence can be made up after prior arrangement with the instructor.
- up to 40 points for laboratory tests
The written exam is taken at the end of semester. It contains simple tasks in the form of concrete questions as to mathematical constructions, constraints of algorithms, basic equations and principles of methods, often in the form of a fill-in quiz.
Two tests are arranged in the laboratory exercises (in the 6th and 12th weeks of the semester) each for max. 20 points. The tests contain tasks corresponding to the content of realised laboratory exercises. There is no lab-test-based qualification limit for the written exam.
Computer exercises are obligatory. Justified absence can be made up after prior arrangement with the instructor.
Aims
The aim of the course is to make students familiar with a high-quality applicative knowledge of advanced techniques of static and dynamic image processing 2D and 3D and to enable them to master both the theory and the practise of advanced techniques of image processing.
On completion of the course, students will be able to:
- describe the basic terms of digital image processing,
- apply the principles of development of image processing algorithms using the OpenCV libraries,
- describe the techniques of intensity transformations,
- define the geometrical transformations of an image,
- use homogenous coordinates,
- interpret the Discrete Fourier Transform of an image and image filtering in the frequency domain,
- describe the terms convolution, filtering and edge detection in the spatial domain,
- describe the basics of image segmentation,
- interpret the mathematical model of the camera,
- describe panoramic image stitching,
- demonstrate the properties of epipolar geometry,
- describe the principles of biometric image identification,
- describe the principles of depth information extraction,
- describe the principle, problems and methods of optical flow estimation.
On completion of the course, students will be able to:
- describe the basic terms of digital image processing,
- apply the principles of development of image processing algorithms using the OpenCV libraries,
- describe the techniques of intensity transformations,
- define the geometrical transformations of an image,
- use homogenous coordinates,
- interpret the Discrete Fourier Transform of an image and image filtering in the frequency domain,
- describe the terms convolution, filtering and edge detection in the spatial domain,
- describe the basics of image segmentation,
- interpret the mathematical model of the camera,
- describe panoramic image stitching,
- demonstrate the properties of epipolar geometry,
- describe the principles of biometric image identification,
- describe the principles of depth information extraction,
- describe the principle, problems and methods of optical flow estimation.
Study aids
Not applicable.
Prerequisites and corequisites
Not applicable.
Basic literature
ŘÍHA, K.: Laboratorní cvičení kurzu Pokročilé techniky zpracování obrazu, VUT v Brně, 2014 (CS)
ŘÍHA, K.: Pokročilé techniky zpracování obrazu, skripka, VUT v Brně, 2012. ISBN: 978-80-214-4894-0 (CS)
ŘÍHA, K.: Pokročilé techniky zpracování obrazu, skripka, VUT v Brně, 2012. ISBN: 978-80-214-4894-0 (CS)
Recommended reading
GONZALEZ, R. C., WOODS, R. E.: Digital Image Processing, Prentice Hall 2002, ISBN: 0201180758. (CS)
HLAVÁČ V., ŠONKA M.: Počítačové vidění, Grada, Praha 1992, ISBN 80-85424-67-3. (CS)
PRATA, S.: Mistrovství v C++, Computer Press 2013, ISBN: 978-80-251-3828-1. (CS)
HLAVÁČ V., ŠONKA M.: Počítačové vidění, Grada, Praha 1992, ISBN 80-85424-67-3. (CS)
PRATA, S.: Mistrovství v C++, Computer Press 2013, ISBN: 978-80-251-3828-1. (CS)
Classification of course in study plans
- Programme MPC-TIT Master's 1 year of study, summer semester, compulsory-optional
Type of course unit
Lecture
26 hod., optionally
Teacher / Lecturer
Syllabus
1. Základní pojmy digitálního zpracování obrazu, algoritmizace zpracování, knihovna OpenCV s C++ API.
2. Jasové transformace, ekvalizace histogramu.
3. Geometrické transformace obrazu, homogenní souřadnice, interpolační techniky.
4. Diskrétní Fourierova Transformace obrazu, filtrace obrazu ve frekvenční oblasti.
5. Zpracování obrazu v prostorové oblasti, konvoluční teorém, filtrace, detekce hran.
6. Základy segmentace, automatické nalezení prahu, Cannyho detektor hran, Viola-Jones objektový detektor.
7. Model kamery, paralelní promítání, perspektivní promítání, translace, rotace, kalibrace.
8. Homografie, skládání panoramatického pohledu: projektivní transformace, válcové panorama, kulové panorama, základy automatického hledání korespondencí.
9. Epipolární geometrie, stereopár obrazů, rektifikace.
10.Biometrická obrazová identifikace, PCA, rozpoznání podle obličeje.
11.Extrakce prostorové informace, korespondenční problém, disparitní prostor, dynamické programování.
12.Optický tok, sledování objektu, pohybu ve scéně, metody dle Horna-Schuncka, Lucase-Kanadeho, Farnebäcka.
Laboratory exercise
39 hod., optionally
Teacher / Lecturer
Syllabus
1. Úvod do OpenCV, prahování
2. Jasové transformace
3. Geometrické transformace obrazu
4. Diskrétní Fourierova Transformace obrazu
5. Zpracování obrazu v prostorové oblasti
6. Test
7. Model kamery, kalibrace
8. Skládání panoramatického pohledu
9. Epipolární geometrie
10. Detekce objektů
11. Optický tok
12. Test