Subjects within computer vision in the research front-line.
FDD3339 Topics in Computer Vision III 9.0 credits

Information per course offering
Information for Autumn 2025 Start 25 Aug 2025 programme students
- Course location
KTH Campus
- Duration
- 25 Aug 2025 - 24 Oct 2025
- Periods
Autumn 2025: P1 (9 hp)
- Pace of study
67%
- Application code
10682
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Course memo is not published
- Number of places
Places are not limited
- Target group
- No information inserted
- Planned modular schedule
- [object Object]
- Schedule
- Schedule is not published
- Part of programme
- No information inserted
Contact
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus FDD3339 (Spring 2019–)Content and learning outcomes
Course contents
Intended learning outcomes
After the course the student should be able to
* ) explain, implement and modify methods and algorithms within computer vision (the focus of the course can vary from time to time),
* ) contrast different methods against one another and choose appropriate method for a given problem (the focus of the course can vary from time to time).
Literature and preparations
Specific prerequisites
The student must carry out research on PhD level within computer vision or a close field.
Literature
Examination and completion
Grading scale
Examination
- EXA1 - Examination, 9.0 credits, grading scale: P, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
If the course is discontinued, students may request to be examined during the following two academic years.
Other requirements for final grade
The requirements are decided by examiner before each course offering.
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.