Image Processing

Postgraduate course

Course description

Objectives 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.

Semester of Instruction

Required Previous Knowledge
The course is based on a least one course in Programming and 40 ECTS Mathematics
Recommended Previous Knowledge

Programming, Calculus, Linear Algebra, and Numerics/Scientific Computing corresponding to the courses INF100, MAT111, MAT112, MAT121, and MAT160

Compulsory Assignments and Attendance
Forms of Assessment
Oral exam. Exercises might be graded and included in the final grade.
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.