Machine vision (4cr)
Code: KL25KONÄKÖ4-3008
General information
- Enrollment
- 17.07.2020 - 06.12.2020
- Registration for the implementation has ended.
- Timing
- 17.08.2020 - 20.12.2020
- Implementation has ended.
- Number of ECTS credits allocated
- 4 cr
- Local portion
- 4 cr
- Mode of delivery
- Contact learning
- Teaching languages
- Finnish
- Degree programmes
- Open University of Applied Sciences
- Teachers
- Juha Hirvonen
- Course
- KL25KONÄKÖ4
Evaluation scale
1-5
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
Will be shared separately during the course.
Comprehensive books are e.g.:
Hornberg, A. 2017. Handbook of Machine and Computer Vision: The Guide for Developers and Users. 2. Weinheim: Wiley-VCH
Szeliski, R. 2010. Computer Vision: Algorithms and Applications. Lontoo: Springer.
OpenCV tutorial pages: https://docs.opencv.org/master/d6/d00/tutorial_py_root.html
Teaching methods
Contact lessons, preliminary tasks for the contact lessons, self-study material, programming exercises, project works and reports
Student workload
28 contact hours
80 hours for self-study, exercises and project works
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.