Image Processing

Language of Instruction

English

Pre-requirements

None

Learning Outcomes

To provide a solid knowledge and understanding of the most important algorithms: the mathematical theory behind them, their numerical stability and efficiency. The course is very useful for master students in computational mathematics.

Contact Information

advice@math.uib.no

Course offered (semester)

Spring

Language of Instruction

English

Aim and Content

The course deals with basic algorithms and mathematical theory that constitute foundation for classical and modern digital image analysis. The classical part of the course deals with understanding digital images, basic manipulations based on the image histogram smoothing and sharpening by spatial filters, elementary image registration. Further, Fourier analysis, Fast Fourier Transformations, wavelet analysis and also digital filter theory will be considered. We also consider edge detection and thresholding. The modern part gives an overview with segmentation using watersheds, noise removal by Rdin-Osher-Fatemi model, graph cuts, optimization models for image registration, active contours and level set methods.

Learning Outcomes

To provide a solid knowledge and understanding of the most important algorithms: the mathematical theory behind them, their numerical stability and efficiency. The course is very useful for master students in computational mathematics.

Pre-requirements

None

Recommended previous knowledge

MAT160 Scientific Computing I

Compulsory Requirements

Exercises

Assessment methods

Written exam. It is opportunity for grades on exercises, which can be included in the final grade. If less than 20 students are taking the course, it can be oral exam.

Grading Scale

The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.

Contact Information

advice@math.uib.no