Computational Image Processing
2014-2015 - IMT6131 - 5 ECTS

On the basis of

  • At least 30 ECTS credits university level mathematics including vector calculus and differential equations
  • Fundamental programming and algorithms
  • Fundamental image processing

Expected learning outcomes

Knowledge

  • The candidate is in the forefront of knowledge within the fields of selected computational image processing techniques
  • The candidate can evaluate the expediency and application of variational image processing methods and processes in research and development projects
  • The candidate has the ability to discuss and explain variational, wavelet and scale-space analytical methods

Skills

  • The candidate can formulate variational image processing problems using partial differential equations
  • The candidate can implement numerical solutions to variational image processing problems

General competence

  • The candidate has the ability to communicate and lead discussions on recent research about computational image processing methods
  • The candidate has the ability to evaluate and critique mechanisms for image modelling and representation

Topic(s)

  • Variational calculus
  • Numerical solutions of PDEs
  • Total variation methods
  • Level-set representations
  • Wavelet and scalespace analysis
  • Image modelling and representation
  • Multiscale image processing
  • Applications to image processing problems such as denoising, deblurring, inpainting, segmentation, image difference, enhancement, gamut mapping, colour correction, demosaicing

Teaching Methods

Lectures
Meeting(s)/Seminar(s)
Tutoring

Form(s) of Assessment

Other

Form(s) of Assessment (additional text)

Candidates must provide one paper where the applicability of the topics of the course to her/his own thesis work is thoroughly discussed. The paper should be in the form of a scientific paper, include examples of the application of computational image processing methods, and preferably constitute a basis for a future publishable scientific paper.

Grading Scale

Pass/Failure

External/internal examiner

Evaluated by an internal examiner.

Re-sit examination

None

Examination support

None

Coursework Requirements

  • On a given topic, prepare and give one seminar consisting of an introductory lecture (1-2 hours) followed by a conducted group discussion.
  • Attend at least 75% of the lectures and seminars.

Teaching Materials

  • Chan, Tony and Jianhong Shen (2005). Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods . Society for Industrial Mathematics.
  • Selected papers (list announced at the beginning of the semester)

Additional information

In case there will be less than 5 candidates that apply for the course, it will be at the discretion of the course responsible whether the course will be offered or not and, if yes, in which form.