# Signal and Image Processing (SIP)

### Course content

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.

Education

MSc programme in Computer Science

MSc programme in Physics

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
• 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.

The course will be a mixture of lectures, pen-and-paper exercises, and programming exercises.

See Absalon when the course is set up.

ECTS
7,5 ECTS
Type of assessment
Continuous assessment
Continuous evaluation of 7 written assignments.
Marking scale
Censorship form
No external censorship
Several internal examiners
##### Criteria for exam assessment

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

Single subject courses (day)

• Category
• Hours
• Lectures
• 38
• Theory exercises
• 12
• Practical exercises
• 12
• Preparation
• 16
• Project work
• 128
• English
• 206