Computational Image Processing
Study plans 2016-2017
- 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
- 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
- The candidate can formulate variational image processing problems using partial differential equations
- The candidate can implement numerical solutions to variational image processing problems
- 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
- 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
Form(s) of Assessment
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.
Evaluated by an internal examiner.
External examiner (or internal and external) will evaluate the paper within 5 years period, next time at latest in 2019.
- 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.
- 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)
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.