Advanced Topics in Image Analysis (ATIA)
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.
- Selected advanced topics in image analysis.
- 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.
- 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.
Lectures and project work.
Active participation is expected.
You should have passed the courses "Machine
Learning"/“Statistical Methods for Machine Learning” and
“Signal and Image Processing”, and “Advanced Deep Learning” or
Academic qualifications equivalent to a BSc degree is recommended.
PhD’s can register for MSc-course by following the same procedure as credit-students, see link above.
- 7,5 ECTS
- Type of assessment
- 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
Criteria for exam assessment
See Learning Outcome.
Single subject courses (day)
- Project work
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 1
- No limit
The number of seats may be reduced in the late registration period
- Study Board of Mathematics and Computer Science
- Department of Computer Science
- Faculty of Science
- Jens Petersen (4-7f77847f4f73783d7a843d737a)
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Courseinformation of students