NDAK15013U Advanced Topics in Image Analysis (ATIA)
Volume 2019/2020
Education
MSc. in Computer Science
Content
The purpose of this course is to expose the student to selected advanced topics in image analysis. The course will bring the student up to a level sufficient for master thesis work within image analysis and computer vision. Focus is not on specific topics, but rather on recent research trends.
Learning Outcome
Knowledge of
- Selected advanced topics in image analysis.
Skills to
- Read, review and understand recent scientific papers.
- Apply the knowledge obtained by reading scientific papers.
- Compare methods from computer vision and image analysis and assess their potentials and shortcommings.
Competences to
- Understand advanced methods, and to transfer the gained knowledge to solutions to small problems.
- Plan and carry out self-learning.
- Present the result of small assignments in scientific writing.
Literature
See Absalon.
Recommended Academic Qualifications
You should have passed the
courses "Machine Learning"/“Statistical Methods for
Machine Learning” and “Signal and Image Processing”, or similar.
Academic qualifications equivalent to a BSc degree is recommended.
Academic qualifications equivalent to a BSc degree is recommended.
Teaching and learning methods
Lectures and project
work.
Workload
- Category
- Hours
- Lectures
- 14
- Preparation
- 90
- Project work
- 102
- Total
- 206
Feedback form
Written
Oral
Individual
Collective
Continuous feedback during the course of the
semester
Sign up
Self Service at KUnet
As an exchange, guest and credit student - click here!
Continuing Education - click here!
PhD’s can register for MSc-course by following the same procedure as credit-students, see link above.
Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Written assignmentThe report is written during the course.
Electronic submission of individual written report. - Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
- Re-exam
Same as the ordinary exam.
Criteria for exam assesment
See Learning Outcome.
Course information
- Language
- English
- Course code
- NDAK15013U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- B
- Course capacity
- No limit
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Contracting faculty
- Faculty of Science
Course Coordinators
- Sune Darkner (darkner@di.ku.dk)
Saved on the
16-09-2019