FDD3337 Topics in Computer Vision I 3.0 credits
Content and learning outcomes
Course contents
Subjects within computer vision in research front-line.
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).
Course disposition
Literature and preparations
Specific prerequisites
The student must carry out research on PhD level within computer vision or a close field.
Recommended prerequisites
Equipment
Literature
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
Examination
- EXA1 - Examination, 3.0 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.
Other requirements for final grade
The requirements are decided by the examiner before each course offering.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
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.
Further information
Course web
Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.
Course web FDD3337