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Main menu for Browse IS/STAG
Course info
KIV / ZVI
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Course description
Department/Unit / Abbreviation
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KIV
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ZVI
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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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Visual Information Processing
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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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Czech
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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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10 / -
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9 / -
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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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0 / -
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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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Summer semester
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Semester taught
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Summer 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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Czech
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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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N/A
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Courses depending on this Course
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KIV/SDSZ, 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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Learn and acquire knowledge of computer vision and image processing. Study basic methods of image acquisition, image enhancement and processing, object detection, scene analysis and understanding.
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Requirements on student
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Credits:
- written test:
- students project development
Exam:
- discussion on students project
- examine questions
- deadline:
In detail:
http://www.kiv.zcu.cz/~novyp/zvi/zvi.html
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Content
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1. Computer vision and basic characteristics, mathematical model of image, image pre-processing, segmentation, recognition, reconstruction (3D), image capturing, analogue to digital conversion (ADC, sampling and quantizing).
2. Image properties, connectivity, neighbourhood, distance, path, connected set of pixels, boundary and boundary extraction, curves and lines on a discrete grid, shape representation, area and perimeter, moments and centre-point of region, eccentricity.
3. Histograms, grey-level transformation, monadic and dyadic operators, image addition, subtraction and multiplication, histogram stretching, shrinking, slide, equalization and specification.
4. Image segmentation, thresholding, automatic thresh detection, transformed histograms, scatter plots, grey-level co-occurrence matrix.
5. Image segmentation, region based segmentation technique, region growing, region operations and detection, region splitting and merging.
6. Image filtration and high-pass filters, edge detection, vertical and horizontal edge, gradient operators (Roberts, Prewitt, Sobel), compass operators, Laplace operator, line and spot detection, image sharpening.
7. Image filtration and low-pass filters, linear and nonlinear digital filtration, noise, signal processing vs. IIR and FIR filter, some characteristics of filtering methods.
8. Mathematical morphology, point set, structuring element, basic morphological operations, erosion, dilation, opening and closing operations, hit and miss transform, thinning and thickening.
9. Thinning and skeleton, medial axis transform, classical thinning algorithm, multiple pixels method of thinning, thinning and skeleton algorithms in the context of the morphological operations.
10.Image representation in the frequency domain, the properties of the two-dimensional Fourier transform, DFT-Discrete Fourier Transform, digital filtration.
11. Chain code for boundaries representation, Freeman?s chain code, differential chain code, Fourier descriptors for shape representation, Run-Length Codes (RLC).
12. Standards of image data format, compression of image.
13. Introduction to pattern recognition, general pattern recognition problem, objects classification.
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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:
Šonka, Milan; Hlaváč, Václav. Počítačové vidění. Praha : Grada, 1992. ISBN 80-85424-67-3.
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Basic:
Hlaváč, Václav; Sedláček, Miloš. Zpracování signálů a obrazů. 1. vyd, dotisk. Praha : Vydavatelství ČVUT, 2001. ISBN 80-01-02114-9.
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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:
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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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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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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Preparation for comprehensive test (10-40)
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20
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Graduate study programme term essay (40-50)
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45
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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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30
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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: |
program at the level of subjects KIV / PPA1, KIV / PPA2 and KIV / PT, eg programming languages Java, C / C ++, C # |
apply methods of probability calculus and statistics and numerical mathematics in the range of subjects KMA / PSA and KMA / NM |
analyze and process signals in the scope of the subject KIV / AZS |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
use programming techniques and data structures and algorithmize tasks |
solve probability calculus problems and statistics, algorithmize numerical mathematics problems |
use basic signal processing techniques, ADC, FIR, IIR, DFT issues |
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: |
understand the principles of machine vision, description, topology and geometry of the image scene |
be familiar with the principles and methods of image filtering in the spatial and frequency domains |
využívat vlastností histogramu pro segmentaci prahováním a jasové transformace |
apply morphological transformations |
perform skeletization and thinning of objects in the image |
Skills - skills resulting from the course: |
implement image filtering in the spatial and frequency domains |
perform luminance transformations, e.g. histogram equalization etc. |
detect the boundaries of objects in the image |
segment images by thresholding or area spacing methods |
define the skeleton of the object and implement algorithms for dilation and erosion of objects |
Competences - competences resulting from the course: |
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: |
Test |
Seminar work |
Combined exam |
Skills - skills achieved by taking this course are verified by the following means: |
Test |
Seminar work |
Individual presentation at a seminar |
Combined exam |
Competences - competence achieved by taking this course are verified by the following means: |
Individual presentation at a seminar |
Seminar work |
Combined exam |
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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 |
Seminar classes |
One-to-One tutorial |
Individual study |
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