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Lecturer(s)
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Štursa Dominik, Ing. Ph.D.
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Doležel Petr, prof. Ing. Ph.D.
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Course content
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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 The content of the exercises is identical to the content of the lectures.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Demonstration, Laboratory work
- Participation in classes
- 120 hours per semester
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Learning outcomes
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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.
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.
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Prerequisites
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Basic knowledge of programming is assumed.
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Assessment methods and criteria
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Oral examination, Home assignment evaluation
Attendance at direct classes is recommended. It is necessary to prepare a project of machine vision task.
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Recommended literature
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BATCHELOR, Bruce G. Machine vision handbook: with 1295 figures and 117 tables (online). London: Springer, 2012.
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BEYERER, J., F. P. LEÓN a Ch. FRESE. Machine Vision: Automated Visual Inspection: Theory Practice and Applications.. Springer, 2016. ISBN 3662508184.
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DAVIES, E. R. Computer Vision: Principlex, Algorithms, Applications, Learning.. Academic Press, 2017. ISBN 978-0128092842.
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Hotař Vlastimil. Úvod do problematiky strojového vidění.. Liberec: Technická univerzita, 2015. ISBN 97-88-07494-156-6.
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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.
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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.
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MCMANAMOM Paul. Field Guide to Lidar. 1. Bellingham. USA: SPIE, 2015. ISBN 9781628416541.
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Szeliski Richard. Computer Vision: Algorithms and Application (online).
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