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Course content

Computer vision is the study of how a machine, such as an unmanned system, can interpret and understand its surrounding environment using visual data such as images and video. This course will give an introduction to this field of study through a thorough theoretical review of the physical imaging process, camera geometry and image processing. We will cover several of the most important computer vision tools, such as image pyramids, keypoint features and robust estimation of two-view geometry. The course also focuses on practical implementation of computer vision systems, and covers several interesting applications, such as object detection, image stitching, 3D reconstruction and Visual SLAM.

Learning outcome

After completing TEK9030

  • you will have a fundamental overview of the field of computer vision
  • you will know about, and understand, how you can apply fundamental computer vision tools and methods
  • you will understand how some important tools and methods work in detail
  • you will be able to implement algorithms that solve basic computer vision problems
  • you will have experience with using the OpenCV library to build computer vision systems

Admission to the course

PhD candidates from the University of Oslo should apply for classes and register for examinations through Studentweb.

If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers’ schools may have specific rules for ranking applicants for courses with limited intake capacity.

PhD candidates who have been admitted to another higher education institution must apply for a position as a visiting student within a given deadline.

The course is based on introductory courses in image processing and image analysis. A good understanding of linear algebra is recommended.

Overlapping courses

Teaching

3 hours of lectures and lab exercises per week throughout the semester. The course has practical experiments and project work using the programming library OpenCV and various camera systems.

The course has project work made up of smaller installments during the term, which must be approved in order to take the final exam.

Flipped classroom is used in this course. Here you will be able to see video lectures on your own, and in your own time, before you attend experiments and guided project work in the set lecture periods.

The course lectures are given at the Department of Technology systems in Kjeller Research Park. See the schedule for the student bus from Campus Blindern.

Examination

  • A final written exam counts 100% towards the final grade.
  • With few candidates, examination may be conducted orally.
  • This course has mandatory exercises that must be approved before you can sit the final exam.

It will also be counted as 1 of the 3 attempts to sit the exam for this course, if you sit the exam for one of the following courses:

Examination support material

No examination support material is allowed.

Language of examination

Courses taught in English will only offer the exam paper in English. You may write your examination paper in Norwegian, Swedish, Danish or English.

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

Resit an examination

Students who can document a valid reason for absence from the regular examination are offered a postponed examination at the beginning of the next semester.

Re-scheduled examinations are not offered to students who withdraw during, or did not pass, the original examination.

Special examination arrangements, use of sources, explanations and appeals

See more about examinations at UiO

Last updated from FS (Common Student System) Sep. 28, 2022 11:24:12 AM

Facts about this course

Credits
10
Level
PhD
Teaching
Spring
Examination
Spring
Teaching language
Norwegian (English on request)