NDAK15013U Advanced Topics in Image Analysis (ATIA)
Volume 2024/2025
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 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.
Literature
See Absalon.
Recommended Academic Qualifications
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.
Academic qualifications equivalent to a BSc degree is recommended.
Teaching and learning methods
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.
Active participation is expected.
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 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 assesment
See Learning Outcome.
Course information
- Language
- English
- Course code
- NDAK15013U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 1
- Schedule
- B
- Course capacity
- No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Contracting faculty
- Faculty of Science
Course Coordinators
- Jens Petersen (phup@di.ku.dk)
Saved on the
26-04-2024