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Lecturer(s)
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Hotař Vlastimil, doc. Ing. Ph.D.
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Course content
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1. Use of technological scene visualization tools in industrial practice and for Industry 4.0. 2. Physical principles of image data acquisition (electromagnetic radiation, acoustic waves, particle radiation, electrical energy, contact methods, ...) 3. Overview of principles and visualization tools of the technological scene using electromagnetic radiation and ultrasound. 4. Sensors for visible spectrum and near ultraviolet and infrared radiation, their types and functions, colour sensing, area and line scan cameras, noise, vignettes, blooming. 5. Sensor sizes, resolution, spectrum range, colour scale, exposure times, shutter, interface, output signal, input signal for camera control, lens mount, resistance to environmental conditions. 6. Intelligent cameras, camera sensors, line scan cameras for visible spectrum. 7. Objective lens, distortion. 8. Exposure and basic scanning parameters. Types of light surces, lighting design procedure. 9. 3D cameras, basic principles of 3D image acquisition. 10. Basic principles of image processing, basics of image processing, thresholding, image preprocessing, mathematical morphology, convolution, transformation, software filters. 11. Examples of the use of machine vision. Use of fractal geometry (fundamentals of fractal geometry, estimation of fractal dimension, application). 12. Interfacing machine vision systems with robots. 13. Communications of the robot and camera control system. 14. Bin-picking and its applications.
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Learning activities and teaching methods
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Self-study (text study, reading, problematic tasks, practical tasks, experiments, research, written assignments), Laboratory work, Lecture, Practicum
- Semestral paper
- 20 hours per semester
- Preparation for credit
- 20 hours per semester
- Preparation for exam
- 25 hours per semester
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Learning outcomes
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The subject gives an overview of principles and possibilities of technology scene visualisation (machine vision) and its application on production lines and robotized workplaces (robot vision). It deals with the basic means of obtaining image data, physical principles of image acquisition, post-processing, analysis, interpretation and evaluation of image data. Overview of chips types, industrial cameras and lighting, lenses, data transfer types, and interface.
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Prerequisites
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Not required.
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Assessment methods and criteria
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Oral exam, Written exam
Credit: attendance minimally 80%, passing the test, semester work. Examination: written and oral, obtaining the credit is a precondition.
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Recommended literature
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GONZALEZ, R. C., WOODS, R. E. Digital Image Processing. 3rd Edition.. New Jersey: Prentice-Hall,, 2008. ISBN 978-0-13-168728-8.
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HLAVÁČ, V., SEDLÁČEK, M. Zpracování signálů a obrazů. ČVUT FEL, Praha, 2001..
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HOTAŘ, V. Metodika popisu průmyslových dat pomocí fraktální geometrie. Liberec: Technická univerzita v Liberci, 2008..
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Hotař, V. Úvod do problematiky strojového vidění. Část 1, Základní principy a hardware. ISBN 978-80-7494-156-6..
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HOTAŘ, V. Úvod do problematiky strojového vidění. Část 2, Základy zpracování obrazu. ISBN 978-80-7494-202-0..
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SONKA, M., HLAVAC, V., BOYLE, R. Image Processing, Analysis, and Machine Vision. Pacific Grove: Books/Cole Publishing Company, 1998. ISBN 0-534-95393-X.
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TYLER, CH. W. Computer vision: from surfaces to 3D objects. Boca Raton: CRC Press, c2011, 250 s..
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WOHLER, CH. 3d computer vision: efficient methods and applications. 2nd. London: Springer, 2013. str. 381..
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ZELINKA, I., VČELAŘ, F., ČANDÍK, M. Fraktální geometrie - principy a aplikace. Praha: Nakladatelství BEN - technická literatura, 2006.. &, &.
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