|
|
Main menu for Browse IS/STAG
Course info
KRP / RSTVI
:
Course description
Department/Unit / Abbreviation
|
KRP
/
RSTVI
|
Academic Year
|
2023/2024
|
Academic Year
|
2023/2024
|
Title
|
Machine Vision
|
Form of course completion
|
Examination
|
Form of course completion
|
Examination
|
Accredited / Credits
|
Yes,
4
Cred.
|
Type of completion
|
Combined
|
Type of completion
|
Combined
|
Time requirements
|
Seminar
20
[Hours/Semester]
|
Course credit prior to examination
|
Yes
|
Course credit prior to examination
|
Yes
|
Automatic acceptance of credit before examination
|
No
|
Included in study average
|
YES
|
Language of instruction
|
Czech
|
Occ/max
|
|
|
|
Automatic acceptance of credit before examination
|
No
|
Summer semester
|
0 / -
|
0 / -
|
0 / -
|
Included in study average
|
YES
|
Winter semester
|
0 / -
|
0 / -
|
0 / 0
|
Repeated registration
|
NO
|
Repeated registration
|
NO
|
Timetable
|
Yes
|
Semester taught
|
Winter semester
|
Semester taught
|
Winter semester
|
Minimum (B + C) students
|
not determined
|
Optional course |
Yes
|
Optional course
|
Yes
|
Language of instruction
|
Czech
|
Internship duration
|
0
|
No. of hours of on-premise lessons |
0
|
Evaluation scale |
A|B|C|D|E|F |
Periodicity |
každý rok
|
Evaluation scale for credit before examination |
S|N |
Periodicita upřesnění |
|
Fundamental theoretical course |
No
|
Fundamental course |
No
|
Fundamental theoretical course |
No
|
Evaluation scale |
A|B|C|D|E|F |
Evaluation scale for credit before examination |
S|N |
Substituted course
|
KRP/NSTVI
|
Preclusive courses
|
N/A
|
Prerequisite courses
|
N/A
|
Informally recommended courses
|
N/A
|
Courses depending on this Course
|
N/A
|
Histogram of students' grades over the years:
Graphic PNG
,
XLS
|
Course objectives:
|
The aim of the course is to introduce the principles of creation and processing of digital photography, demonstration of the specifics of image capture for industrial and scientific applications, introduction of the possibilities of advanced image processing algorithms and methods of designing a functional system for machine vision.
|
Requirements on student
|
Attendance at direct classes is recommended. It is necessary to prepare a project of machine vision task.
|
Content
|
The aim of the course is to introduce the principles of creation and processing of digital photography, demonstration of the specifics of image capture for industrial and scientific applications, introduction of the possibilities of advanced image processing algorithms and methods of designing a functional system for machine vision.
1. The process of creating a digital image
2. Sensors for digital image capture
3. Lenses and their properties
4. Illuminators and their properties
5. Filters and their use
6. Line cameras
7. Representation of digital image, basic operations for image editing
8. Edge enhancement, detection of points and areas of interest, feature extraction
9. Segmentation
10. Object recognition in image data
11. Object tracking, motion modeling
12. Use of laser sensors and IR sensors in machine vision
13. Machine vision system design procedure
|
Activities
|
|
Fields of study
|
V případě mimořádných opatření bude výuka probíhat vzdáleně s využitím programu MS Teams v době dle rozvrhu. Účast na schůzkách skupiny v MS Teams je ekvivalentní účasti na přednáškách a cvičeních.
In the case of distance learning, lessons will be tought trough MS Teams. Lessons will be at the time shown in the timetable. MS Teams is equivalent to participation and or attendens in lectures and excersises.
|
Guarantors and lecturers
|
|
Literature
|
-
Basic:
LUCCI, Stephen a Danny KOPEC. Artificial Intelligence in the 21st Centruy. 2nd Edition. Herndon: Mercury Learning and Information, 2016. ISBN 978-1-942270-00-3.
-
Basic:
LAWLESS, William, Ranjeey MUTTU, Donald; SOFGE, Ira S. MISKOWITZ a Stephen RUSSELL. Artiticial Intellignece for the Internet of Everything. London: Elsevier, 2019. ISBN 978-0-1281-7636-8.
-
Basic:
Szeliski Richard. Computer Vision: Algorithms and Application (online).
-
Basic:
DAVIES, E. R. Computer Vision: Principlex, Algorithms, Applications, Learning.. Academic Press, 2017. ISBN 978-0128092842.
-
Basic:
BEYERER, J., F. P. LEÓN a Ch. FRESE. Machine Vision: Automated Visual Inspection: Theory Practice and Applications.. Springer, 2016. ISBN 3662508184.
-
Basic:
Hotař Vlastimil. Úvod do problematiky strojového vidění.. Liberec: Technická univerzita, 2015. ISBN 97-88-07494-156-6.
-
Recommended:
MCMANAMOM Paul. Field Guide to Lidar. 1. Bellingham. USA: SPIE, 2015. ISBN 9781628416541.
-
Recommended:
BATCHELOR, Bruce G. Machine vision handbook: with 1295 figures and 117 tables (online). London: Springer, 2012.
|
Time requirements
|
All forms of study
|
Activities
|
Time requirements for activity [h]
|
Účast na výuce
|
120
|
Total
|
120
|
|
Prerequisites - other information about course preconditions |
Basic knowledge of programming is assumed. |
Competences acquired |
Basic orientation in the task of machine vision.
Ability to apply the acquired knowledge to typical problems of machine vision - selection of sensor and lens, lighting design, processing of acquired data. |
Teaching methods |
- Monologic (reading, lecture, briefing)
- Demonstration
- Laboratory work
|
Assessment methods |
- Oral examination
- Home assignment evaluation
|
|
|
|