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Lecturer(s)
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Novák Jakub, Ing. Ph.D.
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Chalupa Petr, Ing. Ph.D.
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Course content
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Lectures: 1. Introduction and basic principles of machine vision 2. Image filtering 3. Edges 4. Detection of lines 5. Binary image analysis 6. Image transformations 7. Camera model 8. Camera calibration 9. Hardware of machine vision systems 10. Design of machine vision systems 11. - 13. Implementace systému strojového vidění
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Learning activities and teaching methods
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Lecturing, Projection (static, dynamic), Exercises on PC, Practice exercises, Individual work of students
- Home preparation for classes
- 24 hours per semester
- Term paper
- 35 hours per semester
- Participation in classes
- 56 hours per semester
- Preparation for examination
- 20 hours per semester
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| prerequisite |
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| Knowledge |
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| Expected to have basic knowledge of algorithms, programming, and of fundamental concepts in mathematics and physics. |
| Expected to have basic knowledge of algorithms, programming, and of fundamental concepts in mathematics and physics. |
| learning outcomes |
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| Describe the basic components of the industrial machine vision system |
| Describe the basic components of the industrial machine vision system |
| Explain the basic algorithms of image processing |
| Explain the basic algorithms of image processing |
| Describe the geometric camera model |
| Describe the geometric camera model |
| Explain basic principles of machine vision illumination |
| Explain basic principles of machine vision illumination |
| Describe the methods of image filtering |
| Describe the methods of image filtering |
| Skills |
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| Select suitable components for optical defect detection |
| Select suitable components for optical defect detection |
| Implement algorithms of image processing using the OpenCV library |
| Implement algorithms of image processing using the OpenCV library |
| Process and visualize the digital image data |
| Process and visualize the digital image data |
| Calibrate the camera |
| Calibrate the camera |
| Design the system of automated optical inspection |
| Design the system of automated optical inspection |
| teaching methods |
|---|
| Knowledge |
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| Practice exercises |
| Projection (static, dynamic) |
| Exercises on PC |
| Exercises on PC |
| Practice exercises |
| Projection (static, dynamic) |
| Lecturing |
| Lecturing |
| Individual work of students |
| Individual work of students |
| assessment methods |
|---|
| Oral examination |
| Oral examination |
| Analysis of seminar paper |
| Analysis of seminar paper |
| Analysis of another type of paper written by the student (Casuistry, diary, plan ...) |
| Analysis of another type of paper written by the student (Casuistry, diary, plan ...) |
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Recommended literature
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Forsyth, D., Ponce, j. Computer vision: a modern approach. Upper Saddle Rivers, 2003. ISBN 0130851981.
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Hartley, R., Zisserman, A. Multiple view geometry in computer vision. cambridge, 2003. ISBN 0521540518.
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Howse, J. Learning OpenCV 4 Computer Vision with Python 3. Packt Publishing Ltd. 2020.
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Sankowski, D., Nowakovski, J. Computer vision in robotics and industrial applications.. Singapore, 2014. ISBN 9789814583718.
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Szelinski, R. Computer Vision: Algorithms and Applications. London, 2010. ISBN 9781848829343.
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Šonka, M. ,Hlaváč, V., Boyle, R. Image processing, analysis, and machine vision. Pacific Grove, 1999. ISBN 053495393X.
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