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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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The course is divided into 4 blocks: 1. Introduction - filtering, edge detection 2. Image processing - lien and circle detection, binary image analysis 3. Camera model - perspective projection, camera model, image transformations 4. Machine vision system components - cameras, lenses, lighting, design
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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
- Term paper
- 30 hours per semester
- Preparation for examination
- 32 hours per semester
- Participation in classes
- 19 hours per semester
- Home preparation for classes
- 54 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) |
| Projection (static, dynamic) |
| Practice exercises |
| Practice exercises |
| Exercises on PC |
| Exercises on PC |
| Lecturing |
| Individual work of students |
| Individual work of students |
| Lecturing |
| assessment methods |
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| Analysis of seminar paper |
| Analysis of seminar paper |
| Written examination |
| Written examination |
| Oral examination |
| Oral examination |
| 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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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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