Introduction to Image Processing and Visualization

Teaching semester

Spring

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).

Recommended Previous Knowledge

MAT160, INF250, INF251, INF109 (or INF100)

Compulsory Assignments and Attendance

Exercises

Forms of Assessment

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.

Department

Mathematics, Informatics