Machine VisionLaajuus (5 cr)
Code: TSAR0520
Credits
5 op
Teaching language
- Finnish
Responsible person
- Samppa Alanen
Objective
Main objectives for this course are acquiring knowledge and getting familiar with different types of machine vision systems and solutions (including robotics applications). Studying in this course require fundamentals in field of technology and programming skills. This course enables further studies of the subject in other courses.
EUR-ACE ENGINEERING PRACTICE
Student is familiar and able to use image acquisition, image pre-processing and image analysis functions in machine vision systems with grayscale and color cameras. Student is familiar with hardware component properties (cameras, image processing components, light sources, optics, connections) in machine vision. Student understands functionalities, limits and opportunities in machine vision systems. Student is able to design machine vision system and program machine vision application and algorithms according to end-user requirements. Student is able to design the installation of the machine vision system according to end-user needs (designing optical geometry, choosing camera and lightning options, implementing interface to automation system, designing environmental protection).
Content
Camera and lightning technologies
Optics
Image acquisition
Image analysis
Designing machine vision system
Programming machine vision application and applying machine vision algorithms
Interfaces for external systems
Qualifications
Fundamentals in field of technology, programming skills
Assessment criteria, satisfactory (1)
Sufficient (1): Students is partly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and implementation is significantly incomplete.
Satisfactory (2): Student is mainly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and/or implementation is incomplete.
Assessment criteria, good (3)
Good (3): Student is able to solve machine vision system design and installation issues according to the requirements of the application. Students is able to design and implement machine vision applications in a functional way including component selection, software and installations. Despite functional implementation, selections and/or implementation are not optimal.
Very good (4): Student is able to manage the design and installation challenges of a machine vision system according to the requirements of the application. Students is able to design and implement machine vision applications in a very good way including component selection, software and installations but there are small selection or implementation differences in the solutions compared to the optimal.
Assessment criteria, excellent (5)
Excellent (5): Student is able to master the design and installation of a machine vision system according to the requirements of the application. Student is able to design and implement challenging machine vision applications in an optimal way including component selection, software and installations.
Enrollment
01.11.2024 - 09.01.2025
Timing
13.01.2025 - 18.05.2025
Number of ECTS credits allocated
5 op
Mode of delivery
Face-to-face
Unit
School of Technology
Campus
Lutakko Campus
Teaching languages
- Finnish
Seats
20 - 35
Degree programmes
- Bachelor's Degree Programme in Electrical and Automation Engineering
Teachers
- Samppa Alanen
Groups
-
TSA22SSSähkö- ja automaatiotekniikka (AMK)
-
TSA22SASähkö- ja automaatiotekniikka (AMK)
Objectives
Main objectives for this course are acquiring knowledge and getting familiar with different types of machine vision systems and solutions (including robotics applications). Studying in this course require fundamentals in field of technology and programming skills. This course enables further studies of the subject in other courses.
EUR-ACE ENGINEERING PRACTICE
Student is familiar and able to use image acquisition, image pre-processing and image analysis functions in machine vision systems with grayscale and color cameras. Student is familiar with hardware component properties (cameras, image processing components, light sources, optics, connections) in machine vision. Student understands functionalities, limits and opportunities in machine vision systems. Student is able to design machine vision system and program machine vision application and algorithms according to end-user requirements. Student is able to design the installation of the machine vision system according to end-user needs (designing optical geometry, choosing camera and lightning options, implementing interface to automation system, designing environmental protection).
Content
Camera and lightning technologies
Optics
Image acquisition
Image analysis
Designing machine vision system
Programming machine vision application and applying machine vision algorithms
Interfaces for external systems
Learning materials and recommended literature
Materials in the e-learning environment.
Teaching methods
- independent study
- distance learning
- webinars
- small group learning
- laboratory work
- learning tasks
Exam dates and retake possibilities
The possible date and method of the exam will be announced in the course opening.
Alternative completion methods
The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.
Student workload
One credit (1 Cr) corresponds to an average of 27 hours of work.
- lectures 40h
- independent study and assignments 50h
- laboratory work and small group learning 45h
Total 135 h
Further information for students
The evaluation is based on the qualitative evaluation of the exam and exercises/assignments.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1): Students is partly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and implementation is significantly incomplete.
Satisfactory (2): Student is mainly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and/or implementation is incomplete.
Evaluation criteria, good (3-4)
Good (3): Student is able to solve machine vision system design and installation issues according to the requirements of the application. Students is able to design and implement machine vision applications in a functional way including component selection, software and installations. Despite functional implementation, selections and/or implementation are not optimal.
Very good (4): Student is able to manage the design and installation challenges of a machine vision system according to the requirements of the application. Students is able to design and implement machine vision applications in a very good way including component selection, software and installations but there are small selection or implementation differences in the solutions compared to the optimal.
Evaluation criteria, excellent (5)
Excellent (5): Student is able to master the design and installation of a machine vision system according to the requirements of the application. Student is able to design and implement challenging machine vision applications in an optimal way including component selection, software and installations.
Prerequisites
Fundamentals in field of technology, programming skills
Enrollment
01.11.2024 - 09.01.2025
Timing
13.01.2025 - 18.05.2025
Number of ECTS credits allocated
5 op
Mode of delivery
Face-to-face
Unit
School of Technology
Campus
Lutakko Campus
Teaching languages
- English
Seats
20 - 35
Degree programmes
- Bachelor's Degree Programme in Electrical and Automation Engineering
Teachers
- Samppa Alanen
Groups
-
TAR22S1Bachelor's Degree Programme in Automation and Robotics
Objectives
Main objectives for this course are acquiring knowledge and getting familiar with different types of machine vision systems and solutions (including robotics applications). Studying in this course require fundamentals in field of technology and programming skills. This course enables further studies of the subject in other courses.
EUR-ACE ENGINEERING PRACTICE
Student is familiar and able to use image acquisition, image pre-processing and image analysis functions in machine vision systems with grayscale and color cameras. Student is familiar with hardware component properties (cameras, image processing components, light sources, optics, connections) in machine vision. Student understands functionalities, limits and opportunities in machine vision systems. Student is able to design machine vision system and program machine vision application and algorithms according to end-user requirements. Student is able to design the installation of the machine vision system according to end-user needs (designing optical geometry, choosing camera and lightning options, implementing interface to automation system, designing environmental protection).
Content
Camera and lightning technologies
Optics
Image acquisition
Image analysis
Designing machine vision system
Programming machine vision application and applying machine vision algorithms
Interfaces for external systems
Learning materials and recommended literature
Materials in the e-learning environment.
Teaching methods
- independent study
- distance learning
- webinars
- small group learning
- laboratory work
- learning tasks
Exam dates and retake possibilities
The possible date and method of the exam will be announced in the course opening.
Alternative completion methods
The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.
Student workload
One credit (1 Cr) corresponds to an average of 27 hours of work.
- lectures 40h
- independent study and assignments 50h
- laboratory work and small group learning 45h
Total 135 h
Further information for students
The evaluation is based on the qualitative evaluation of the exam and exercises/assignments.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1): Students is partly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and implementation is significantly incomplete.
Satisfactory (2): Student is mainly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and/or implementation is incomplete.
Evaluation criteria, good (3-4)
Good (3): Student is able to solve machine vision system design and installation issues according to the requirements of the application. Students is able to design and implement machine vision applications in a functional way including component selection, software and installations. Despite functional implementation, selections and/or implementation are not optimal.
Very good (4): Student is able to manage the design and installation challenges of a machine vision system according to the requirements of the application. Students is able to design and implement machine vision applications in a very good way including component selection, software and installations but there are small selection or implementation differences in the solutions compared to the optimal.
Evaluation criteria, excellent (5)
Excellent (5): Student is able to master the design and installation of a machine vision system according to the requirements of the application. Student is able to design and implement challenging machine vision applications in an optimal way including component selection, software and installations.
Prerequisites
Fundamentals in field of technology, programming skills
Enrollment
01.08.2024 - 22.08.2024
Timing
01.08.2024 - 31.12.2024
Number of ECTS credits allocated
5 op
Virtual portion
3 op
Mode of delivery
40 % Face-to-face, 60 % Online learning
Unit
School of Technology
Campus
Main Campus
Teaching languages
- Finnish
Degree programmes
- Bachelor's Degree Programme in Electrical and Automation Engineering
Teachers
- Samppa Alanen
- Juho Riekkinen
Groups
-
TSA22KMASähkö- ja automaatiotekniikka (AMK)
-
TSA22KMSSähkö- ja automaatiotekniikka (AMK)
Objectives
Main objectives for this course are acquiring knowledge and getting familiar with different types of machine vision systems and solutions (including robotics applications). Studying in this course require fundamentals in field of technology and programming skills. This course enables further studies of the subject in other courses.
EUR-ACE ENGINEERING PRACTICE
Student is familiar and able to use image acquisition, image pre-processing and image analysis functions in machine vision systems with grayscale and color cameras. Student is familiar with hardware component properties (cameras, image processing components, light sources, optics, connections) in machine vision. Student understands functionalities, limits and opportunities in machine vision systems. Student is able to design machine vision system and program machine vision application and algorithms according to end-user requirements. Student is able to design the installation of the machine vision system according to end-user needs (designing optical geometry, choosing camera and lightning options, implementing interface to automation system, designing environmental protection).
Content
Camera and lightning technologies
Optics
Image acquisition
Image analysis
Designing machine vision system
Programming machine vision application and applying machine vision algorithms
Interfaces for external systems
Learning materials and recommended literature
Materials in the e-learning environment.
Teaching methods
- independent study
- distance learning
- webinars
- small group learning
- laboratory work
- learning tasks
Exam dates and retake possibilities
The possible date and method of the exam will be announced in the course opening.
Alternative completion methods
The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.
Student workload
One credit (1 Cr) corresponds to an average of 27 hours of work.
- remote lectures 15h
- independent study and assignments 75h
- laboratory work and small group learning 45h
Total 135 h
Further information for students
The evaluation is based on the qualitative evaluation of the exam and exercises/assignments.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1): Students is partly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and implementation is significantly incomplete.
Satisfactory (2): Student is mainly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and/or implementation is incomplete.
Evaluation criteria, good (3-4)
Good (3): Student is able to solve machine vision system design and installation issues according to the requirements of the application. Students is able to design and implement machine vision applications in a functional way including component selection, software and installations. Despite functional implementation, selections and/or implementation are not optimal.
Very good (4): Student is able to manage the design and installation challenges of a machine vision system according to the requirements of the application. Students is able to design and implement machine vision applications in a very good way including component selection, software and installations but there are small selection or implementation differences in the solutions compared to the optimal.
Evaluation criteria, excellent (5)
Excellent (5): Student is able to master the design and installation of a machine vision system according to the requirements of the application. Student is able to design and implement challenging machine vision applications in an optimal way including component selection, software and installations.
Prerequisites
Fundamentals in field of technology, programming skills
Timing
07.03.2023 - 31.12.2024
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Online learning
Unit
School of Technology
Campus
Main Campus
Teaching languages
- Finnish
Degree programmes
- Bachelor's Degree Programme in Electrical and Automation Engineering
Teachers
- Samppa Alanen
- Juho Riekkinen
Groups
-
ZJA23SLIKEROKAvoin amk, Like, Liiketoiminnallisesti kestävän robotiikan kehittäjä
-
ZJAJ23KLIKEROKLiiketoiminnallisesti kestävän robotiikan kehittäjä
Objectives
Main objectives for this course are acquiring knowledge and getting familiar with different types of machine vision systems and solutions (including robotics applications). Studying in this course require fundamentals in field of technology and programming skills. This course enables further studies of the subject in other courses.
EUR-ACE ENGINEERING PRACTICE
Student is familiar and able to use image acquisition, image pre-processing and image analysis functions in machine vision systems with grayscale and color cameras. Student is familiar with hardware component properties (cameras, image processing components, light sources, optics, connections) in machine vision. Student understands functionalities, limits and opportunities in machine vision systems. Student is able to design machine vision system and program machine vision application and algorithms according to end-user requirements. Student is able to design the installation of the machine vision system according to end-user needs (designing optical geometry, choosing camera and lightning options, implementing interface to automation system, designing environmental protection).
Content
Camera and lightning technologies
Optics
Image acquisition
Image analysis
Designing machine vision system
Programming machine vision application and applying machine vision algorithms
Interfaces for external systems
Evaluation scale
Pass/Fail
Evaluation criteria, satisfactory (1-2)
Sufficient (1): Students is partly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and implementation is significantly incomplete.
Satisfactory (2): Student is mainly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and/or implementation is incomplete.
Evaluation criteria, good (3-4)
Good (3): Student is able to solve machine vision system design and installation issues according to the requirements of the application. Students is able to design and implement machine vision applications in a functional way including component selection, software and installations. Despite functional implementation, selections and/or implementation are not optimal.
Very good (4): Student is able to manage the design and installation challenges of a machine vision system according to the requirements of the application. Students is able to design and implement machine vision applications in a very good way including component selection, software and installations but there are small selection or implementation differences in the solutions compared to the optimal.
Evaluation criteria, excellent (5)
Excellent (5): Student is able to master the design and installation of a machine vision system according to the requirements of the application. Student is able to design and implement challenging machine vision applications in an optimal way including component selection, software and installations.
Prerequisites
Fundamentals in field of technology, programming skills
Enrollment
20.11.2023 - 04.01.2024
Timing
01.01.2024 - 05.05.2024
Number of ECTS credits allocated
5 op
Virtual portion
3 op
Mode of delivery
40 % Face-to-face, 60 % Online learning
Unit
School of Technology
Campus
Main Campus
Teaching languages
- Finnish
Degree programmes
- Bachelor's Degree Programme in Electrical and Automation Engineering
Teachers
- Samppa Alanen
- Juho Riekkinen
Groups
-
TSA21SASähkö- ja automaatiotekniikka (AMK)
-
TSA21SBSähkö- ja automaatiotekniikka (AMK)
Objectives
Main objectives for this course are acquiring knowledge and getting familiar with different types of machine vision systems and solutions (including robotics applications). Studying in this course require fundamentals in field of technology and programming skills. This course enables further studies of the subject in other courses.
EUR-ACE ENGINEERING PRACTICE
Student is familiar and able to use image acquisition, image pre-processing and image analysis functions in machine vision systems with grayscale and color cameras. Student is familiar with hardware component properties (cameras, image processing components, light sources, optics, connections) in machine vision. Student understands functionalities, limits and opportunities in machine vision systems. Student is able to design machine vision system and program machine vision application and algorithms according to end-user requirements. Student is able to design the installation of the machine vision system according to end-user needs (designing optical geometry, choosing camera and lightning options, implementing interface to automation system, designing environmental protection).
Content
Camera and lightning technologies
Optics
Image acquisition
Image analysis
Designing machine vision system
Programming machine vision application and applying machine vision algorithms
Interfaces for external systems
Learning materials and recommended literature
Materials in the e-learning environment.
Teaching methods
- independent study
- distance learning
- webinars
- small group learning
- laboratory work
- learning tasks
Exam dates and retake possibilities
The possible date and method of the exam will be announced in the course opening.
Alternative completion methods
The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.
Student workload
One credit (1 Cr) corresponds to an average of 27 hours of work.
- remote lectures 10h
- independent study and assignments 80h
- laboratory work and small group learning 45h
Total 135 h
Further information for students
The evaluation is based on the qualitative evaluation of the exam and exercises/assignments.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1): Students is partly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and implementation is significantly incomplete.
Satisfactory (2): Student is mainly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and/or implementation is incomplete.
Evaluation criteria, good (3-4)
Good (3): Student is able to solve machine vision system design and installation issues according to the requirements of the application. Students is able to design and implement machine vision applications in a functional way including component selection, software and installations. Despite functional implementation, selections and/or implementation are not optimal.
Very good (4): Student is able to manage the design and installation challenges of a machine vision system according to the requirements of the application. Students is able to design and implement machine vision applications in a very good way including component selection, software and installations but there are small selection or implementation differences in the solutions compared to the optimal.
Evaluation criteria, excellent (5)
Excellent (5): Student is able to master the design and installation of a machine vision system according to the requirements of the application. Student is able to design and implement challenging machine vision applications in an optimal way including component selection, software and installations.
Prerequisites
Fundamentals in field of technology, programming skills
Enrollment
01.08.2023 - 24.08.2023
Timing
04.09.2023 - 19.12.2023
Number of ECTS credits allocated
5 op
Virtual portion
3 op
Mode of delivery
40 % Face-to-face, 60 % Online learning
Unit
School of Technology
Campus
Main Campus
Teaching languages
- Finnish
Seats
0 - 25
Degree programmes
- Bachelor's Degree Programme in Electrical and Automation Engineering
Teachers
- Samppa Alanen
- Juho Riekkinen
Groups
-
TSA21KMBSähkö- ja automaatiotekniikka (AMK)
-
TSA21KMASähkö- ja automaatiotekniikka (AMK)
Objectives
Main objectives for this course are acquiring knowledge and getting familiar with different types of machine vision systems and solutions (including robotics applications). Studying in this course require fundamentals in field of technology and programming skills. This course enables further studies of the subject in other courses.
EUR-ACE ENGINEERING PRACTICE
Student is familiar and able to use image acquisition, image pre-processing and image analysis functions in machine vision systems with grayscale and color cameras. Student is familiar with hardware component properties (cameras, image processing components, light sources, optics, connections) in machine vision. Student understands functionalities, limits and opportunities in machine vision systems. Student is able to design machine vision system and program machine vision application and algorithms according to end-user requirements. Student is able to design the installation of the machine vision system according to end-user needs (designing optical geometry, choosing camera and lightning options, implementing interface to automation system, designing environmental protection).
Content
Camera and lightning technologies
Optics
Image acquisition
Image analysis
Designing machine vision system
Programming machine vision application and applying machine vision algorithms
Interfaces for external systems
Learning materials and recommended literature
Materials in the e-learning environment.
Teaching methods
- independent study
- distance learning
- webinars
- small group learning
- laboratory work
- learning tasks
Exam dates and retake possibilities
The possible date and method of the exam will be announced in the course opening.
Alternative completion methods
The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.
Student workload
One credit (1 Cr) corresponds to an average of 27 hours of work.
- remote lectures 10h
- independent study and assignments 80h
- laboratory work and small group learning 45h
Total 135 h
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1): Students is partly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and implementation is significantly incomplete.
Satisfactory (2): Student is mainly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and/or implementation is incomplete.
Evaluation criteria, good (3-4)
Good (3): Student is able to solve machine vision system design and installation issues according to the requirements of the application. Students is able to design and implement machine vision applications in a functional way including component selection, software and installations. Despite functional implementation, selections and/or implementation are not optimal.
Very good (4): Student is able to manage the design and installation challenges of a machine vision system according to the requirements of the application. Students is able to design and implement machine vision applications in a very good way including component selection, software and installations but there are small selection or implementation differences in the solutions compared to the optimal.
Evaluation criteria, excellent (5)
Excellent (5): Student is able to master the design and installation of a machine vision system according to the requirements of the application. Student is able to design and implement challenging machine vision applications in an optimal way including component selection, software and installations.
Prerequisites
Fundamentals in field of technology, programming skills
Enrollment
01.11.2022 - 05.01.2023
Timing
09.01.2023 - 30.04.2023
Number of ECTS credits allocated
5 op
Mode of delivery
Face-to-face
Unit
School of Technology
Campus
Main Campus
Teaching languages
- Finnish
Degree programmes
- Bachelor's Degree Programme in Electrical and Automation Engineering
Teachers
- Samppa Alanen
- Juho Riekkinen
Groups
-
TSA20SABachelor's Degree Programme in Electrical and Automation Engineering
-
TSA20SBBachelor's Degree Programme in Electrical and Automation Engineering
Objectives
Main objectives for this course are acquiring knowledge and getting familiar with different types of machine vision systems and solutions (including robotics applications). Studying in this course require fundamentals in field of technology and programming skills. This course enables further studies of the subject in other courses.
EUR-ACE ENGINEERING PRACTICE
Student is familiar and able to use image acquisition, image pre-processing and image analysis functions in machine vision systems with grayscale and color cameras. Student is familiar with hardware component properties (cameras, image processing components, light sources, optics, connections) in machine vision. Student understands functionalities, limits and opportunities in machine vision systems. Student is able to design machine vision system and program machine vision application and algorithms according to end-user requirements. Student is able to design the installation of the machine vision system according to end-user needs (designing optical geometry, choosing camera and lightning options, implementing interface to automation system, designing environmental protection).
Content
Camera and lightning technologies
Optics
Image acquisition
Image analysis
Designing machine vision system
Programming machine vision application and applying machine vision algorithms
Interfaces for external systems
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1): Students is partly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and implementation is significantly incomplete.
Satisfactory (2): Student is mainly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and/or implementation is incomplete.
Evaluation criteria, good (3-4)
Good (3): Student is able to solve machine vision system design and installation issues according to the requirements of the application. Students is able to design and implement machine vision applications in a functional way including component selection, software and installations. Despite functional implementation, selections and/or implementation are not optimal.
Very good (4): Student is able to manage the design and installation challenges of a machine vision system according to the requirements of the application. Students is able to design and implement machine vision applications in a very good way including component selection, software and installations but there are small selection or implementation differences in the solutions compared to the optimal.
Evaluation criteria, excellent (5)
Excellent (5): Student is able to master the design and installation of a machine vision system according to the requirements of the application. Student is able to design and implement challenging machine vision applications in an optimal way including component selection, software and installations.
Prerequisites
Fundamentals in field of technology, programming skills
Timing
16.03.2022 - 14.08.2022
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Online learning
Unit
School of Technology
Campus
Main Campus
Teaching languages
- Finnish
Degree programmes
- Bachelor's Degree Programme in Electrical and Automation Engineering
Teachers
- Juho Riekkinen
Groups
-
ZJA21SLKRKAvoin AMK, Liiketoiminnallisesti kestävän robotiikan kehittäjä
Objectives
Main objectives for this course are acquiring knowledge and getting familiar with different types of machine vision systems and solutions (including robotics applications). Studying in this course require fundamentals in field of technology and programming skills. This course enables further studies of the subject in other courses.
EUR-ACE ENGINEERING PRACTICE
Student is familiar and able to use image acquisition, image pre-processing and image analysis functions in machine vision systems with grayscale and color cameras. Student is familiar with hardware component properties (cameras, image processing components, light sources, optics, connections) in machine vision. Student understands functionalities, limits and opportunities in machine vision systems. Student is able to design machine vision system and program machine vision application and algorithms according to end-user requirements. Student is able to design the installation of the machine vision system according to end-user needs (designing optical geometry, choosing camera and lightning options, implementing interface to automation system, designing environmental protection).
Content
Camera and lightning technologies
Optics
Image acquisition
Image analysis
Designing machine vision system
Programming machine vision application and applying machine vision algorithms
Interfaces for external systems
Evaluation scale
Pass/Fail
Evaluation criteria, satisfactory (1-2)
Sufficient (1): Students is partly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and implementation is significantly incomplete.
Satisfactory (2): Student is mainly able to design machine vision system and take into account the requirements of the application. Students is able to design and implement hardware, software and installations for machine vision applications but the design and/or implementation is incomplete.
Evaluation criteria, good (3-4)
Good (3): Student is able to solve machine vision system design and installation issues according to the requirements of the application. Students is able to design and implement machine vision applications in a functional way including component selection, software and installations. Despite functional implementation, selections and/or implementation are not optimal.
Very good (4): Student is able to manage the design and installation challenges of a machine vision system according to the requirements of the application. Students is able to design and implement machine vision applications in a very good way including component selection, software and installations but there are small selection or implementation differences in the solutions compared to the optimal.
Evaluation criteria, excellent (5)
Excellent (5): Student is able to master the design and installation of a machine vision system according to the requirements of the application. Student is able to design and implement challenging machine vision applications in an optimal way including component selection, software and installations.
Prerequisites
Fundamentals in field of technology, programming skills