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Course info
KIV / AOS
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Course description
Department/Unit / Abbreviation
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KIV
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AOS
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Image Analysis and Scene Understanding
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Form of course completion
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Exam
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Form of course completion
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Exam
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Accredited / Credits
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Yes,
6
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Lecture
3
[Hours/Week]
Tutorial
2
[Hours/Week]
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Course credit prior to examination
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Yes
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Course credit prior to examination
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Yes
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Automatic acceptance of credit before examination
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No
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Included in study average
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YES
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Language of instruction
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-
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Occ/max
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Automatic acceptance of credit before examination
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No
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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YES
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Winter semester
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0 / -
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2 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Winter semester
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Semester taught
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Winter semester
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Minimum (B + C) students
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10
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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-
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Internship duration
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0
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No. of hours of on-premise lessons |
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Evaluation scale |
1|2|3|4 |
Periodicity |
každý rok
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Evaluation scale for credit before examination |
S|N |
Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
Yes
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Fundamental theoretical course |
No
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Evaluation scale |
1|2|3|4 |
Evaluation scale for credit before examination |
S|N |
Substituted course
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None
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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KIV/ZVI or KKY/ZDO
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Courses depending on this Course
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KIV/AVD, KIV/MISZ, KIV/ZOM
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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Extend acquired knowledge of computer vision and image processing. Study, analyze and apply in practice methods of image segmentation and filtration, shape representation, objects detection and measuring, principles and methods of computed tomography and IR-computer vision.
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Requirements on student
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Credits:
- students project development
- deadline: 09. 02. 2024
Exam:
- students project presentation and discussion
- examine questions
In detail:
http://www.kiv.zcu.cz/~novyp/aos/aos.html
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Content
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1. Automatic threshold selection and segmentation methods, optimal methods of grey-level histograms and it extension to multithresholding.
2. Automatic threshold selection and segmentation methods, methods based on entropy of the histogram.
3. Automatic threshold selection and segmentation methods, methods based on minimum error thresholding.
4. Image filtration, methods based on co-occurrence matrix and entropy of image.
5. Fourier transformation, discrete Fourier transformation, properties, amplitude, phases and power spectrum, image filtration.
6. Shape descriptions, digital closed parametric curves, basic shape descriptors, chain code, centriod distance, curvature signature, cumulative angular function, area function, complex coordinates of curve.
7. Shape descriptions, digital closed parametric curves, functions of coordinates of x and y axes, Fourier expansion and Fourier coefficients, Fourier descriptors, invariance, reconstruction of the curve, classification.
8. Detection of particles, particle tracks on the dosimeter-target, the problems and methods of particle counting, probability model and application.
9. Computed tomography principles, conventional X-ray systems, X-ray technique for determining three-dimensional structure, Radon transform, principles and technique of image reconstruction from projections.
10. Computed tomography, technique of image reconstruction from projections, Fourier Slice Theorem, summation methods, ART, SART, MART, filtered back projection. Development of the CT scanner, generation characteristics of CT scanners and CT applications.
11. Theory of the thermoviewer, infrared emission, blackbody as an ideal object with the highest emissivity, noctovision and thermovision.
12. Theory of thermoviewer, IR detectors, materials for IR, transmission of IR through atmosphere, optical system of thermoviewer, emissivity and reflection from environment correction, specifications of thermoviewer.
13. Classification technique, pattern recognition, symbolic scene description, image understanding.
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Activities
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Fields of study
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Guarantors and lecturers
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Literature
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Basic:
Umbaugh, Scott E. Digital Image Processing and Analysis: Applications with MATLAB and CVIPtools. Boca Raton: Taylor & Francis, 2018. ISBN 978-1-4987-6602-9.
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Extending:
Rosenfeld, A.; Kak, A.C. Digital Picture Processing. Academic Press, New York, 1982.
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Extending:
Opeenheim, V.A.; Schafer, R.W. Digital Signal Processing. Prentice-Hall, Inc, 1975.
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Extending:
Low, A. Introductory Computer Vision and Image Processing. McGraw-Hill Book Company, London, 1991.
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Recommended:
Umbaugh, Scott E. Computer imaging : digital image analysis and processing. Boca Raton : Taylor & Francis, 2005. ISBN 0-8493-2919-1.
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Recommended:
Jain, K.A. Fundamentals of Digital Image Processing. Prentice-Hall, Inc, 1989.
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Recommended:
Serra, J. Image Analysis and Mathematical Morphology. Academic Press, New York, 1982.
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Recommended:
Sonka, Milan; Boyle, Roger; Hlavac, Vaclav. Image processing, analysis, and machine vision. 2nd ed. Pacific Grove : PWS Publishing, 1999. ISBN 0-534-95393-X.
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Recommended:
Dobeš, Michal. Zpracování obrazu a algoritmy v C#. 1. vyd. Praha : BEN - technická literatura, 2008. ISBN 978-80-7300-233-6.
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Recommended:
Hlaváč, Václav; Sedláček, Miloš. Zpracování signálů a obrazů. 1. vyd, dotisk. Praha : Vydavatelství ČVUT, 2001. ISBN 80-01-02114-9.
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On-line library catalogues
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Contact hours
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65
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Preparation for an examination (30-60)
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45
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Graduate study programme term essay (40-50)
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50
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Total
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160
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
používat metody číslicového zpracování signálů a obrazů v rozsahu předmětů KIV/AZS a KIV/ZVI |
využívat získané vědomosti z teorie informace, např v rozsahu předmětu KIV/TI |
ovládat programovací techniky, viz KIV/PT, a programovací jazyk Java nebo C/C++, C# |
řešit úlohy z numerické matematiky a počtu pravděpodobnosti a statistiky, viz předměty KMA/NM, KMA/PSA |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
porozumět principům strojového vidění, popisu, topologii a geometrii obrazové scény |
orientovat se v principech a metodách filtrace snímků v prostorové a frekvenční oblasti |
využívat vlastností histogramu pro segmentaci prahováním a jasové transformace |
aplikovat morfologické transformace a provádět skeletizaci a ztenčování objektů ve snímku |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
N/A |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
orientovat se v rozšiřujících metodách v oblasti segmentace a filtrace snímků |
používat metody Fourier Descriptors pro popis ploch, jejich rekonstrukci a rozpoznávání a klasifikaci |
porozumět principům počítačové tomografie |
aplikovat metody úběžníkového a rovnoběžného promítání |
seznámit se s principem termovizní techniky |
Skills - skills resulting from the course: |
algoritmizovat úlohy automatického prahování |
provádět výpočet DFT a Fourier Descriptors, rekonstruovat hranice objektů a testovat metody klasifikace |
vytvářet algoritmy pro rekonstrukci řezů metodami CT |
řešit úlohu odhadu parametrů 3D objektu metodami úběžníkového a rovnoběžného promítání a referenčního tělesa |
testovat metody odhadu počtu objektů ve snímku, praktická úloha dozimetrie |
Competences - competences resulting from the course: |
N/A |
N/A |
N/A |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Seminar work |
Individual presentation at a seminar |
Combined exam |
Skills - skills achieved by taking this course are verified by the following means: |
Combined exam |
Seminar work |
Individual presentation at a seminar |
Competences - competence achieved by taking this course are verified by the following means: |
Combined exam |
Individual presentation at a seminar |
Seminar work |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Interactive lecture |
One-to-One tutorial |
Seminar classes |
Self-study of literature |
Skills - the following training methods are used to achieve the required skills: |
Interactive lecture |
Seminar classes |
Individual study |
One-to-One tutorial |
Competences - the following training methods are used to achieve the required competences: |
Interactive lecture |
One-to-One tutorial |
Individual study |
Seminar classes |
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