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Computer vision

Individual course

Max amount of FITech students: 50

Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course.

The course gives an overview of algorithms, models and methods which are used in automatic analysis of visual data.

Course contents

  • Image formation and processing
  • Feature detection and matching
  • Motion estimation
  • Structure-from-motion
  • Object recognition
  • Image-based 3D reconstruction

Learning outcomes

After the course, the student

  • is familiar with basic concepts and methods of computer vision.
  • understands the basic principles of image-based 3D reconstruction.
  • is familiar with techniques used for automatic object recognition from images.
  • can design and implement common computer vision methods and apply them to practical problems with real-world image data.

Course material

Lecture material is partially based on the following books:

  • R. Szeliski: Computer Vision: Algorithms and Applications (link)
  • Hartley & Zisserman: Multiple View Geometry in Computer Vision (link)

Teaching schedule

The course has lectures, weekly exercises and an exam. You can complete everything but the exam online. Participation in the teaching sessions is not obligatory. Returning the weekly homework is necessary.

  • Lectures (Otaniemi), Mondays at 8:15–10
  • Exercise sessions (Otaniemi), Fridays at 12:15–14
  • Guidance sessions (Otaniemi), Thursdays at 14:15–16
  • Exam (Otaniemi), Friday 15.12. at 9–12

More information on Aalto University’s course page.

You can get a digital badge after completing this course.

konenäkö AI tekoäly kuvan prosessointi visuaalinen data algoritmit

Responsible teacher

Aalto University
Juho Kannala

Further information about the studies

Aalto University
Kirsi Viitaharju

Contact person for applications

FITech Network University
Fanny Qvickström, Student services specialist
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ICT studies
AI and machine learning
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0 €
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Aalto University
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Adult learner,
Degree student
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General prerequisites:
Programming skills and basic knowledge of data structures and mathematics (linear algebra, probability) are necessary.
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