Machine vision (5 cr)

Evaluation scale

0-5

Objective

The student knows and is able to use the image acquisition, preprocessing and image analysis methods for grey scale and color cameras. He/she knows the properties of machine vision components (cameras, image processing components, light sources, optics, interfaces). The student understands the functions of a machine vision system, its limitations and opportunities and is able to do the hardware design and algorithm programming according to the requirements of the end user.

EA-EN:Engineering analysis
The student is able to analyze the production or quality control process and choose the suitable machine vision solution.

EA-EE:Engineering design
The student is able to select and engineer the machine vision components, system as a whole and do the programming of machine vision algorithms.

EA-ER:Engineering practice
The student is able to select the suitable machine vision components to each application and take into account the needs of installation environment .

Content

The content areas are camera and light source technology, optical engineering, camera and light source selections, image acquisition, image processing, machine vision system design, programming of machine vision application and algorithms, interface to automation systems, robots or manipulators. The installation requirements and environmental shielding is also covered in the course content.

Materials

Lecture material
Additional material from literature and internet
For example https://www.wiley.com/en-us/Machine+Vision+Algorithms+and+Applications%2C+2nd+Edition-p-9783527413652

Teaching methods

Contact teaching focuses on machine vision theory and practical exercises. The implementation also includes plenty of group assignments in laboratory and independently made exercises to be returned. In short, implementation includes independent studying, working in groups and attending contact lessons.

Student workload

A total of 135 hours of student work in the course, including about half of contact lessons and half of independent studying.

Assessment criteria, satisfactory (1)

Sufficient (1):The student’s hardware and software design of machine vision system and installation is of poor quality and contains deficiencies.

Satisfactory (2):The student is mainly able to do the hardware and software design of machine vision system and installation according to the requirements of the application. The student is able to design and implement partly the machine vision application concerning component selections, programming and installation but the results are incomplete.

Assessment criteria, good (3)

Good (3):The student can solve of the hardware and software design of machine vision system and installation needs according to the requirements of the application. The student is able to design and implement the machine vision applications using feasible and working solutions concerning component selections, programming and installation.

Very good (4):The student shows mastering of the hardware and software design of machine vision system and installation aspects according to the requirements of the application. The student is able to design and implement the machine vision applications using very good solutions concerning component selections, programming and installation.

Assessment criteria, excellent (5)

Excellent (5): The student masters the hardware and software design of machine vision system and installation aspects according to the requirements of the application. The student is able to design and implement the machine vision applications in optimal way concerning component selections, programming and installation.

Qualifications

Basic studies of automation engineering including LabView – graphic programming tool

Further information

Self-assessments and peer reviews are made within exercises and learning tasks. The assessment decision is based on the exercises to be returned, laboratory work and exam.