NDAA09027U Signal and Image Processing (SIP)

Volume 2015/2016
Education

MSc programme in Computer Science

MSc programme in Physics

Content

Signal and Image Processing introduce the basic computer tools to deal with sampled data, especially 2 dimensional images, and 1D application examples will are presented.

Applications of Signal and image processing range from time series analysis include sound or financial data to complex imaging devices, from astronomy to surveillance to medicine and biology. This make this course an ideal supplement for students coming from various fields of science.

Students will acquire a theoretical knowledge of signal processing and good implementation practices. There will be weekly exercises, both theoretical, so as to help students understand the different concepts, as well as practical ones, so as to apply them, including filtering, denoising, segmentation.

Learning Outcome

Knowledge

  • 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
  • Radon transform
  • Linear and non-linear spatial transformaitons of images
  • Mathematical morphology

 

Skills

  • Apply basic signal processing methods to solve basic signal processing problems.


Competences

  • 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 when the course is set up.

The course will be a mixture of lectures, pen-and-paper exercises, and programming exercises.
  • Category
  • Hours
  • Lectures
  • 38
  • Practical exercises
  • 12
  • Preparation
  • 16
  • Project work
  • 128
  • Theory exercises
  • 12
  • Total
  • 206
Credit
7,5 ECTS
Type of assessment
Continuous assessment
Continuous evaluation of 7 written assignments.
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Re-exam

Oral examination (25 minutes including grading) in course curriculum without preparation. Internal grading using the 7-point grading scale.

Criteria for exam assesment

To get maximum grade the student must successfully be able to:

  • Discuss and apply the theoretical basics of digital signal and image processing

  • Reflect the linear processing of signals and design and apply digital filters for discrete signals.

  • Explain and identify different types of noise, design noise removal algorithms for image restoration and solve statistical linear inverse filtering problems for images.

  • Compare Fourier analysis to multiresolution analysis, relate the fundamental concepts of multiresolution analysis, and perform time/space-frequency analysis for signals and images.

  • Analyze the image histograms, and transform the images to another forms to enhance the visual content of the image or to facilitate easier interpretation and processing.

  • Explain fundamental image segmentation approaches and implement them to extract homogeneous regions on the images.

  • Relate and illustrate elementary representation methods in description of image content