Machine Vision

University of Oulu
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Course overview

 

ECTS Credits 5   cr
Language of instruction FI.

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 Heikkilä Janne Mustaniemi Janne
Work placements

No.

Other information

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University of Oulu oulun.yliopisto(at)oulu.fi
Tel. +358 294 48 0000
Fax +358 8 553 4112
PL 8000
FI-90014 Oulun yliopisto
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