NDAK12002U Vision and Image Processing
The objective is to provide students with a general introduction
to visual cognitive models and its relevance to modern image
processing. The course also provides students with the necessary
mathematical background to understand vision and image processing
methods and includes programming exercises.
This course is mandatory for students enrolled in the It &
cognition MSc study programme and is a restricted 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).
We will investigate state of the art methods for solving visual
processing tasks such as perception, scene understanding, object
recognition, and content based image search and
retrieval.
The student will obtain the following from this course:
Knowledge:
- Theoretical and practical knowledge of the current research within computer vision and image analysis.
- Knowledge of common application areas.
Skills:
- The ability to read and apply the knowledge obtained by reading scientific papers.
- The ability to convert a theoretical algorithmic description into a concrete program implementation.
- The ability to compare computer vision and image analysis algorithms and assess their ability to solve a specific task.
Competences:
- understanding and analyzing the main challenges in vision and image processing today
See Absalon.
- Category
- Hours
- Exam
- 83
- Lectures
- 32
- Practical exercises
- 8
- Preparation
- 83
- Total
- 206
As
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Continuing Education - click here!
- Credit
- 7,5 ECTS
- Type of assessment
- Continuous assessmentContinuous assessment based on 4-6 assignments throughout the course. Assignments handled electronically in Absalon.
- Marking scale
- passed/not passed
- Censorship form
- No external censorship
Several external examiners
- Re-exam
- Resubmission of assignments.
Criteria for exam assesment
In order to pass, the student must document knowledge of the most common problems, methods and results in vision and image processing including:
- describing common applications of importance to society
- describing and applying feature extraction methods and modeling techniques in image and vision processing
- analyzing the main challenges in vision and image processing today
- Implementation of selected methods
- Comparative evaluation of the studied methods.
Course information
- Language
- English
- Course code
- NDAK12002U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- C (Mon 13-17 + Wednes 8-17)
- Course capacity
- No limit
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Course responsibles
- Kim Steenstrup Pedersen (6-6e6c7076777343676c316e7831676e)