Course: Machine Vision

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Course title Machine Vision
Course code ITE/PVI
Organizational form of instruction Lecture + Lesson
Level of course Master
Year of study not specified
Semester Winter
Number of ECTS credits 5
Language of instruction Czech, English
Status of course Compulsory, Compulsory-optional
Form of instruction Face-to-face
Work placements Course does not contain work placement
Recommended optional programme components None
Lecturer(s)
  • Paleček Karel, Ing. Ph.D.
  • Chaloupka Josef, doc. Ing. Ph.D.
Course content
1. Digital image processing, computer graphic versus machine vision, image digitization, digital image properties, color and gray-scale image 2. Introduction to radiometry, 2D image, digital cameras, optic, CCD and CMOS chips 3. Color spaces, geometric transformation, pixel co-ordinate transformation, brightness interpolation 4. Pixel brightness transformations, linear discrete image transforms, 2D FFT and DCT, Hadamard transform, Wavelets 5. Image smoothing, edge detectors I. 6. Edge detectors II., image restoration 7. Segmentation, thresholding, edge-based and region-based segmentation, matching 8. Binary mathematical morphology 9. Gray-scale mathematical morphology 10. Skeletons and object marking, granulometry, morphological segmentation and watersheds 11. Region identification 12. Utilization of 2D convolution and correlation, region detection 13. Features for image recognition 14. Image objects recognition

Learning activities and teaching methods
Monological explanation (lecture, presentation,briefing)
  • Class attendance - 56 hours per semester
Learning outcomes
The subject Machine Vision is focused on student's ability to understand basic principles of computer image processing and recognition.
Theoretic piece of knowledge and practical skills from requered areas
Prerequisites
unspecified

Assessment methods and criteria
Written exam

Requirements for getting a credit are activity at the seminars. Examination is of the written forms.
Recommended literature
  • DAVIES, E., R.. Computer and Machine Vision, Fourth Edition: Theory, Algorithms, Practicalities.. UK, 2012. ISBN 978-0123869081.
  • HLAVÁČ, Václav a Miloš SEDLÁČEK. Zpracování signálů a obrazů. 2. přeprac. vyd.. ČR, 2007. ISBN 978-80-01-03110-0.
  • CHALOUPKA, J. Přednášky, cvičení - PVI.
  • ŠONKA, Milan, Václav HLAVÁČ a Roger BOYLE. Image processing, analysis, and machine vision. 3rd ed.. Toronto: Thomson, 2008. ISBN 978-0-495-08252-1.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Mechatronics, Informatics and Interdisciplinary Studies Study plan (Version): Information Technology (2013) Category: Informatics courses 2 Recommended year of study:2, Recommended semester: Winter
Faculty: Faculty of Mechatronics, Informatics and Interdisciplinary Studies Study plan (Version): Mechatronics (2016) Category: Special and interdisciplinary fields 2 Recommended year of study:2, Recommended semester: Winter
Faculty: Faculty of Mechatronics, Informatics and Interdisciplinary Studies Study plan (Version): Automatic Control and Applied Computer Science (2016) Category: Special and interdisciplinary fields 2 Recommended year of study:2, Recommended semester: Winter