Advanced Topics in Image Analysis (ATIA)
Course 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.
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 shortcomings.
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.
The focus of this course is on problem-based learning, with a
combination of lecturing, supervision, student presentations, and
peer feedback.
Active participation is expected.
See Absalon.
You should have passed the courses "Machine
Learning"/“Statistical Methods for Machine Learning” and
“Signal and Image Processing”, and “Advanced Deep Learning” or
similar.
Academic qualifications equivalent to a BSc degree is
recommended.
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.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Written assignment
- Type of assessment details
- The written assignment is an individual report written during the course.
- Aid
- All aids allowed
The use of Large Language Models (LLM)/Large Multimodal Models (LMM) – such as ChatGPT and GPT-4 – is permitted.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
- Re-exam
-
Same as the ordinary exam.
Criteria for exam assessment
See Learning Outcome.
Single subject courses (day)
- Category
- Hours
- Lectures
- 14
- Preparation
- 90
- Project work
- 102
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NDAK15013U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 1
- Schedulegroup
-
B
- Capacity
- No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
- Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Contracting faculty
- Faculty of Science
Course Coordinator
- Jens Petersen (4-7f77847f4f73783d7a843d737a)
Er du BA- eller KA-studerende?
Kursusinformation for indskrevne studerende