Study Guide

Objective

After completing the course the student will know the essential machine vision methods and understand their common applications. Application examples are shown in the fields of technology, medicine and biology, to name a few. The student is able to implement image processing and machine vision applications by using OpenCV library and Python programming language.

Content

- Image formation and the structure of the digital image
- Preprocessing algorithms
- Segmentation algorithms
- Morphology algorithms
- Shape and feature detection and identification
- Image transformations

Materials

To be distributed during the course
OpenCV documentation pages

Teaching methods

- theory sessions with example exercises to solve together
- compulsory home exercises and going them through together in exercise sessions
- laboratory group work
- individual project work

Student workload

Contact sessions 40h
Lab exercise 10h
Project work
Home exercises
Other independent studying

Evaluation scale

1-5

Assessment criteria, satisfactory (1)

The student can programmatically read a digital image and perform simple pre-processing and post-processing operations on it, as well as perform simple segmentation. The student knows the basics of the structure of a digital image.

Assessment criteria, good (3)

The student can also analyze a segmented image using different methods and perform measurements from it. The student knows several different pre-processing, post-processing and analysis methods and knows how to apply them. The student has extensive knowledge of machine vision applications and can describe them. The student's programming style is clear.

Assessment criteria, excellent (5)

The student has a strong command of the methods studied in the course and knows how to apply them widely for their own purposes and beyond the applications taught in the course. In addition, the student implements their method very clearly, following a good programming style.

Assessment methods and criteria

Home exercises, laboratory work and project work are all assessed in the scale 1-5. The final grade of the course is the weighted average of them. The grade of the home exercises is based on the numbers or solved exercises, the grade of the lab work and project work is based on the extent of the work.

Assessment criteria, satisfactory (1)

The student fails at solving sufficient number of home exercises and/or acceptable laboratory exercise and/or acceptable project work.

Assessment criteria, good (3)

The student can name the basics of image formation
The student can open an image and repeat the methods used during the course with different parameters to their own images
The student can read the image stream from a machine vision camera and count automatically the objects seen in the image.

In practice: the weighted average of home exercises, laboratory work and project work is 1-2

Assessment criteria, excellent (5)

The student can explain the basics of image formation and the structure of the image in the computer memory.
The student can open an image or multiple images and apply the methods used during the course to their own images in various ways.
The student can read the image stream from a machine vision camera and count automatically the objects seen in the image and classify them based on their shape.

In practice: the weighted average of home exercises, laboratory work and project work is 3-4

Assessment criteria, approved/failed

The student can explain the basics of image formation and the structure of the image in the computer memory.
The student can open an image or multiple images and apply the methods used during the course to their own images in various ways.
The student can read the image stream from a machine vision camera and count automatically the objects seen in the image, classify them based on their shape and measure and commit different measurements based on the contours.

In practice: the weighted average of home exercises, laboratory work and project work is 5

Qualifications

Basics of Programming 1

Further information

Recommended optional programme components:

Basics of Programming 2