Pre-requisites:
A solid background on geometry processing or differential geometry will be of advantage but is not required. Students who haven't followed any related courses (Differential Geometry I, Scientific Visualization, ...) can follow the seminar but should be willing to invest more time.
Description:
In this seminar we will work on recent topics and results that take its place in the field of geometric statistics: the statistical analysis of data being elements of nonlinear geometric spaces.
Such data arises frequently for example in form of anatomical shapes in medical image processing or human body motion in computer vision.
Often, sizable empirical improvements can be observed when the geometry of the data spaces is incorporated into the design of the model and its algorithmic treatment.
Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing geometric data.
The goal of this seminar will be to obtain an in-depth knowledge about the core methodology in geometric statistics as well as an overview of state-of-the-art methods.
The student will acquire practical skills in reading, presenting, explaining, and discussing scientific papers.
The seminar may be used as a preparation for an MSc thesis topic.