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521466S Machine Vision, 5 ECTS cr 
Code 521466S  Validity 01.01.1950 -
Name Machine Vision  Abbreviation Machine Vision 
Scope5 ECTS cr   
TypeAdvanced Studies Discipline4307 Information Engineering 
TypeCourse   
  Grading1 - 5, pass, fail 
 
   
Unit Computer Science and Engineering DP 

Teachers
Name
Heikkilä, Janne Tapani 

Description
ECTS Credits 

5 ECTS cr

 
Language of instruction 

English

 
Timing 

Spring, period  3.

 
Learning outcomes 

After completing the course, student

1. can utilize common machine vision methods for various image analysis problems

2. can detect and recognize objects using features computed from images

3. can use motion information in image analysis

4. can use model matching in image registration and object recognition

5. can explain the basics of geometric computer vision

6. can calibrate cameras

7. can use stereo imaging for 3D reconstruction

8. can use Matlab for implementing basic machine vision algorithms

 
Contents 

Course provides an introduction to machine vision, and its applications to practical image analysis problems. Common computer vision methods and algorithms as well as principles of image formation are studied. Topics: 1. Introduction, 2. Imaging and image representation, 3. Color and shading, 4. Image features, 5. Recognition, 6. Texture, 7. Motion from 2D image sequences, 8. Matching in 2D, 9. Perceiving 3D from 2D images, 10. 3D reconstruction.

 
Mode of delivery 

Face-to-face teaching, homework assignments.

 
Learning activities and teaching methods 

Lectures (20 h), exercises (16 h) and Matlab homework assignments (16 h).  

 
Target group 

Computer Science and Engineering students and other Students of the University of Oulu.

 
Prerequisites and co-requisites 

521467A Digital Image Processing

 
Recommended optional programme components 

521289S Machine Learning. This courses provide complementary information on machine learning methods applied in machine vision. It is recommended to be studied simultaneously.

 
Recommended or required reading 

Lecture notes and exercise material. The following books are recommended for further information: 1) Shapiro, L.G., Stockman,G.C.: Computer Vision, Prentice Hall, 2001. 2) R. Szeliski: Computer Vision: Algorithms and Applications, Springer, 2011. 3) D.A. Forsyth & J. Ponce: Computer Vision: A Modern Approach, Prentice Hall, 2002.

 
Assessment methods and criteria 

The course is passed with final exam and accepted homework assignments.

Read more about assessment criteria at the University of Oulu webpage.

 
Grading 

The course unit utilizes a numerical grading scale 1-5. In the numerical scale zero stands for a fail.

 
Person responsible 

Janne Heikkilä

 
Working life cooperation 

No.

 
Other information 

-

 


Current and future instruction
Functions Name Type ECTS cr Teacher Schedule
registration period has not begun Machine Vision  Course  Heikkilä, Janne Tapani  08.01.18 -08.03.18

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