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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. Hardware of machine vision systems 3. Design of machine vision system 4. Image transformations 5. Camera models 6. Camera calibration 7. Image filtering 8. Edges 9. Lines 10. Binary image analysis 11. Morphology 12. Features 13. Stereo Vision
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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
- 12 hours per semester
- Term paper
- 24 hours per semester
- Participation in classes
- 56 hours per semester
- Preparation for examination
- 48 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 |
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| Knowledge |
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| Projection (static, dynamic) |
| Lecturing |
| Lecturing |
| Practice exercises |
| Exercises on PC |
| Individual work of students |
| Individual work of students |
| Projection (static, dynamic) |
| Exercises on PC |
| Practice exercises |
| assessment methods |
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| Written examination |
| Oral examination |
| Oral examination |
| Analysis of another type of paper written by the student (Casuistry, diary, plan ...) |
| Written examination |
| Analysis of seminar paper |
| Analysis of seminar paper |
| Analysis of another type of paper written by the student (Casuistry, diary, plan ...) |
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Recommended literature
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Corke, P. Robotics, vision and control: fundamental algorithms in Matlab. Berlin, 2011. ISBN 9783642201431.
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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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Sankowski, D., Nowakovski, J. Computer vision in robotics and industrial applications.. Singapore, 2014. ISBN 9789814583718.
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Solomon, C., Breckon, T. Fundamentals of digital image processing: a practical approach with examples in Matlab. Hoboken, 2011. ISBN 9780470844724.
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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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