NDAK12002U Vision and Image Processing (VIP)
MSc Programme in IT and Cognition
Vision and Image Processing (VIP) gives an overview of modern vision techniques used in man and machine. Focus is both on conceptual understanding of the models and methods, and on practical experience. The course covers state of the art methods for image analysis including how to solve visual processing tasks such as object recognition and content-based image search and retrieval.
The course is not focused on providing a deep mathematical understanding of the techniques but will include the mathematical background necessary to understand vision and image processing.
Through a number of mandatory programming exercises, the students will develop simple programs and obtain solutions to non-trivial vision tasks. After the course, the students will be able to understand the models and principles of vision technologies used in new products and applications.
This course is mandatory for students enrolled in the IT and Cognition MSc study programme and is an elective course for students enrolled in the MSc programme in Computer Science. The course content does not overlap with Signal and Image Processing (MSc in Computer Science).
- Theoretical and practical knowledge of the current research within computer vision and image analysis
- Common application areas
- Read and apply the knowledge obtained by reading scientific papers
- Convert a theoretical algorithmic description into a concrete program implementation
- Compare computer vision and image analysis algorithms and assess their ability to solve a specific task
- Understanding and analyzing the main challenges in vision and image processing today
- Describing common applications of importance to society
- Describing and applying feature extraction methods and modelling techniques in image and vision processing
- Analyzing the main challenges in vision and image processing today.
- Implementation of selected methods
See Absalon for a list of course literature.
Academic qualifications equivalent to a BSc degree is recommended.
- Practical exercises
PhD’s can register for MSc-course by following the same procedure as credit-students, see link above.
- 7,5 ECTS
- Type of assessment
- Continuous assessment
- Type of assessment details
- Continuous assessment based on 4-6 written group- and individual assignments throughout the course, where all assignments must be passed.
- All aids allowed
The use of Large Language Models (LLM)/Large Multimodal Models (LMM) – such as ChatGPT and GPT-4 – is permitted.
- Marking scale
- passed/not passed
- Censorship form
- No external censorship
Several internal examiners
The re-exam consists of two parts:
1. Resubmission of all assignments no later than 3 weeks before the re-exam week
2. A 20 minutes oral examination (including grading) without preparation in the re-exam week
Criteria for exam assesment
See "Learning outcome".
- Course code
- 7,5 ECTS
- Full Degree Master
- 1 block
- Block 2
- Course capacity
- 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
- Serge Belongie (8-696c7376756e706c476b70727c356c6b7c)
- Hang Yin (4-6b647c6c43676c316e7831676e)