Objectives and Content
The course deals with basic techniques within digital image analysis and visualization.
Image analysis: 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.
Visualization: The course addresses central aspects of scientific and information visualization. These include the visualization of: volume data (for ex., medical), vector and tensor data (for ex., flow data), and abstract data (for ex., tables).
Compulsory Assignments and Attendance
Forms of Assessment
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
Type of assessment: Oral examination