Course Catalog 2009-2010 International Basic Pori International Postgraduate Open University |Degrees|     |Study blocks|     |Courses|

Person responsible

Timo Prusi

Implementations

	Lecture times and places	Target group recommended to
Implementation 1	Per 4 : Tuesday 16 - 18, K3114 Wednesday 14 - 16, K3114 Thursday 8 - 10, K3114 Monday 8 - 16, Department's computer class

Requirements

Written examination based on lectures and exercises. Accepted laboratory assignments.

Principles and baselines related to teaching and learning

-

Learning outcomes

Basic knowledge and readiness for applying and using machine vision in different discrete parts production applications.

Content

Content	Core content	Complementary knowledge	Specialist knowledge
1.	Properties and selection principles between different camera types. Measurement accuracy and resolution selection, dynamic properties of detectors.
2.	Properties and selection principles between different vision systems. Special properties of vision software.
3.	Light source types, lighting methods. Selection of optics and imaging geometry.
4.	Image analysis basics: digital image, filtering, connection analysis, morphology, convolution, segmentation, (spatial) features, 2D-recognition and position measurement.
5.	Principles and basics of programming vision systems.

Evaluation criteria for the course

All laboratory assignments have to completed successfully and taking part in meetings related assignments are mandatory. Part of the assignments will be graded and they give a part of the total course grade. However, the written exam gives the majority of the total course grade.

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Study material

Type	Name	Author	ISBN	URL	Edition, availability, ...	Examination material	Language
Lecture slides	Machine Vision in Production Automation	Timo Prusi					English
Other literature	Articles and other texts dealing with machine automation	Prusi, NN					English

Correspondence of content

Additional information

This course and TTE-5210 Konenäkö tuotantoautomaatiossa are parallel courses: they have the same contents, they use the same materials (in English), they have the same assignments, etc. The only difference is the language in which the lectures are given.

More precise information per implementation

	Description	Methods of instruction	Implementation
Implementation 1	This course aims to give the students basic knowledge and readiness to use and apply machine vision in different applications in the field discrete product automation. During this course we will, among other things, have a look at different camera types, illumination methods and light sources, factors affecting the measurement resolution and accuracy, programming of vision systems, and also basics of image analysis from machine vision point-of-view. This course is parallel to course TTE-5210 Konenäkö tuotantotekniikassa. The only differences between these two courses are the language and the implementation of the lectures: the content of the lectures, the exercises, and the assignments are the same on both courses. I hope that those of you who understand Finnish would participate the lectures of the Finnish course (TTE-5210) instead of lectures of this course. If you want, you can make the assignments (and the exam) in English if you want to have the credit points from this English course.	Lectures Excercises Laboratory assignments Other contact teaching Study journal, portfolio and other literary work	Contact teaching: 0 % Distance learning: 2 % Self-directed learning: 0 %

Last modified	13.03.2009
Modifier	Timo Prusi