NDAK15013U Advanced Topics in Image Analysis (ATIA)

Volume 2024/2025

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.

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.
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.
  • Category
  • Hours
  • Lectures
  • 14
  • Preparation
  • 90
  • Project work
  • 102
  • Total
  • 206
Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Written assignment
Type of assessment details
The written assignment is an individual report written during the course.
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

Same as the ordinary exam.

Criteria for exam assesment

See Learning Outcome.