NDAA09027U Signal and Image Processing (SIP)
MSc Programme in Computer Science
MSc Programme in Physics
The course introduces basic computational, statistical, and mathematical techniques for representing, modeling, and analysing signals and images. Signals and images are measurements, which change with time and/or space, and these measurements typically originate from a physical system ordered on a grid. Examples are 1-dimensional sound, 2-dimensional images from a consumer camera, 3-dimensional reconstructions from medical scanners, and movies.
Applications include: removal of high frequency noise in sound, detecting and segmenting objects in images, and reconstruction of 3-dimensional computed tomography images (CT) from X-ray images.
- Signal and image processing fundamentals.
- Sampling, Sampling theorem, Fourier transform.
- Convolution, linear and nonlinear filtering.
- Image restoration, inverse filtering.
- Image histograms.
- Image segmentation.
- Multiresolution processing.
- Linear and non-linear spatial transformations of images.
- Mathematical morphology.
- Apply basic signal processing methods to solve basic signal processing problems.
- Evaluate which signal / image processing methods and pipeline of methods is best suited for solving a given signal problem.
- Understand the implications of theoretical theorems and being able to analyse real problems on that basis.
See Absalon for a list of course literature.
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 assessmentContinuous evaluation of 7 written assignments.
The assignments must be individually approved. The final grade is based on an overall assessment.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
Oral examination (25 minutes including grading) in course curriculum without preparation. Internal grading using the 7-point grading scale.
Criteria for exam assesment
See Learning Outcome.
- Theory exercises
- Practical exercises
- Project work