|
Lecturer(s)
|
-
Novák Jakub, Ing. Ph.D.
-
Chalupa Petr, Ing. Ph.D.
|
|
Course content
|
The course is divided into 4 blocks: 1. Introduction, Perspective projection, camera model, image transformations 2. Image processing, filtering, edge detection, binary image analysis 3. Hardware of machine vision system 4. Machine vision system design
|
|
Learning activities and teaching methods
|
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
|
| prerequisite |
|---|
| Knowledge |
|---|
| 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 |
|---|
| 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 |
|---|
| 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 |
|---|
| Projection (static, dynamic) |
| Projection (static, dynamic) |
| Practice exercises |
| Practice exercises |
| Exercises on PC |
| Exercises on PC |
| Individual work of students |
| Individual work of students |
| Lecturing |
| Lecturing |
| assessment methods |
|---|
| Analysis of another type of paper written by the student (Casuistry, diary, plan ...) |
| Analysis of seminar paper |
| Analysis of seminar paper |
| Oral examination |
| Oral examination |
| Written examination |
| Written examination |
| Analysis of another type of paper written by the student (Casuistry, diary, plan ...) |
|
Recommended literature
|
-
Forsyth, D., Ponce, j. Computer vision: a modern approach. Upper Saddle Rivers, 2003. ISBN 0130851981.
-
Hartley, R., Zisserman, A. Multiple view geometry in computer vision. cambridge, 2003. ISBN 0521540518.
-
Sankowski, D., Nowakovski, J. Computer vision in robotics and industrial applications.. Singapore, 2014. ISBN 9789814583718.
-
Szelinski, R. Computer Vision: Algorithms and Applications. London, 2010. ISBN 9781848829343.
-
Šonka, M. ,Hlaváč, V., Boyle, R. Image processing, analysis, and machine vision. Pacific Grove, 1999. ISBN 053495393X.
|