Study Guide

Objective

After completing the course the student will;
- Know the different machine vision technologies and knows where they are usually applied in industry
- Know the key components and their functions in the machine vision system
- Be able to use a machine vision software to create basic level image analysis and extract features from the image

Content

The basic principles of the machine vision, applications of the machine vision, typical components of the machine vision system, machine vision software, defining a machine vision system. The basic functions of the Cognex In-Sight EasyBuilder machine vision software. Basics of the machine vision laboratory equipment.

Materials

Lecture handouts, machine vision related manuals, articles, videos

Teaching methods

Lectures, interactive assignments, software exercises, laboratory assignments. Part of the course can be complete remotely.

Student workload

Total work load of the course: 95 h (scheduled lectures 35 h, independent work 60 h)

Content scheduling

- Fundamental principles of the machine vision
- Applications of the machine vision
- Typical components of the machine vision system
- Basic functions of the Cognex InSight EasyBuilder machine vision software.
- Laboratory exercises

Evaluation scale

1-5

Assessment criteria, satisfactory (1)

The student knows the basics of the course contents.The student knows the principle of machine vision and knows where it can be applied.

Assessment criteria, good (3)

The student knows well the course contents.The student knows the principle of machine vision and knows where it can be applied. The student is able to use programs related to machine vision.

Assessment criteria, excellent (5)

The student knows excellent the course contents.The student knows the principle of machine vision and knows where it can be applied. The student is able to use programs related to machine vision. The student is able to design and implement vision-based applications that implement separation of parts and materials or quality control.

Assessment methods and criteria

Personal or group-based theory, software and laboratory assignments. Evaluation criteria will be announced at the beginning of the course

Further information

The issues of automation technology are internationally similar.

Objective

After completing the course the student will;
- Know the different machine vision technologies and knows where they are usually applied in industry
- Know the key components and their functions in the machine vision system
- Be able to use a machine vision software to create basic level image analysis and extract features from the image

Content

The basic principles of the machine vision, applications of the machine vision, typical components of the machine vision system, machine vision software, defining a machine vision system. The basic functions of the Cognex In-Sight EasyBuilder machine vision software. Basics of the machine vision laboratory equipment.

Materials

Lecture handouts, machine vision related manuals, articles, videos

Teaching methods

Lectures, interactive assignments, software exercises, laboratory assignments. Part of the course can be complete remotely.

Student workload

Total work load of the course: 100 h (scheduled lectures 40 h, independent work 60 h)

Content scheduling

- Fundamental principles of the machine vision
- Applications of the machine vision
- Typical components of the machine vision system
- Basic functions of the Cognex InSight EasyBuilder machine vision software.
- Laboratory exercises

Evaluation scale

1-5

Assessment criteria, satisfactory (1)

The student knows the basics of the course contents.The student knows the principle of machine vision and knows where it can be applied.

Assessment criteria, good (3)

The student knows well the course contents.The student knows the principle of machine vision and knows where it can be applied. The student is able to use programs related to machine vision.

Assessment criteria, excellent (5)

The student knows excellent the course contents.The student knows the principle of machine vision and knows where it can be applied. The student is able to use programs related to machine vision. The student is able to design and implement vision-based applications that implement separation of parts and materials or quality control.

Assessment methods and criteria

Personal or group-based theory, software and laboratory assignments. Evaluation criteria will be announced at the beginning of the course

Further information

The issues of automation technology are internationally similar.

Objective

After completing the course the student will;
- Know the different machine vision technologies and knows where they are usually applied in industry
- Know the key components and their functions in the machine vision system
- Be able to use a machine vision software to create basic level image analysis and extract features from the image

Content

The basic principles of the machine vision, applications of the machine vision, typical components of the machine vision system, machine vision software, defining a machine vision system. The basic functions of the Cognex In-Sight EasyBuilder machine vision software. Basics of the machine vision laboratory equipment.

Materials

Lecture handouts, machine vision related manuals, articles, videos

Teaching methods

Lectures, interactive assignments, software exercices, laboratory assignments

Student workload

Total work load of the course: 100 h (scheduled lectures 40 h, independent work 60 h)

Content scheduling

- Fundamental principles of the machine vision
- Applications of the machine vision
- Typical components of the machine vision system
- Basic functions of the Cognex InSight EasyBuilder machine vision software.
- Laboratory exercises

Evaluation scale

1-5

Assessment criteria, satisfactory (1)

The student knows the basics of the course contents.The student knows the principle of machine vision and knows where it can be applied.

Assessment criteria, good (3)

The student knows well the course contents.The student knows the principle of machine vision and knows where it can be applied. The student is able to use programs related to machine vision.

Assessment criteria, excellent (5)

The student knows excellent the course contents.The student knows the principle of machine vision and knows where it can be applied. The student is able to use programs related to machine vision. The student is able to design and implement vision-based applications that implement separation of parts and materials or quality control.

Assessment methods and criteria

Personal or group-based theory, software and laboratory assignments. Evaluation criteria will be announced at the beginning of the course

Further information

The issues of automation technology are internationally similar.

Objective

After completing the course the student will;
- Know the different machine vision technologies and knows where they are usually applied in industry
- Know the key components and their functions in the machine vision system
- Be able to use a machine vision software to create basic level image analysis and extract features from the image

Content

The basic principles of the machine vision, applications of the machine vision, typical components of the machine vision system, machine vision software, defining a machine vision system. The basic functions of the Cognex In-Sight EasyBuilder machine vision software. Basics of the machine vision laboratory equipment.

Evaluation scale

1-5

Assessment criteria, satisfactory (1)

The student knows the basics of the course contents.The student knows the principle of machine vision and knows where it can be applied.

Assessment criteria, good (3)

The student knows well the course contents.The student knows the principle of machine vision and knows where it can be applied. The student is able to use programs related to machine vision.

Assessment criteria, excellent (5)

The student knows excellent the course contents.The student knows the principle of machine vision and knows where it can be applied. The student is able to use programs related to machine vision. The student is able to design and implement vision-based applications that implement separation of parts and materials or quality control.

Further information

The issues of automation technology are internationally similar.