3 citations · 10 across the 5 of their papers we have counts for
8 papers
Bi-invariant Dissimilarity Measures for Sample Distributions in Lie Groups
Martin Hanik, Hans-Christian Hege, Christoph von Tycowicz
Data sets sampled in Lie groups are widespread, and as with multivariate data, it is important for many applications to assess the differences between the sets in terms of their di…
De Casteljau's Algorithm in Geometric Data Analysis: Theory and Application
Martin Hanik, Esfandiar Nava-Yazdani, Christoph von Tycowicz
For decades, de Casteljau's algorithm has been used as a fundamental building block in curve and surface design and has found a wide range of applications in fields such as scienti…
Manifold GCN: Diffusion-based Convolutional Neural Network for Manifold-valued Graphs
Martin Hanik, Gabriele Steidl, Christoph von Tycowicz
We propose two graph neural network layers for graphs with features in a Riemannian manifold. First, based on a manifold-valued graph diffusion equation, we construct a diffusion l…
Intrinsic shape analysis in archaeology: A case study on ancient sundials
Martin Hanik, Benjamin Ducke, Hans-Christian Hege +2
This paper explores a novel mathematical approach to extract archaeological insights from ensembles of similar artifact shapes. We show that by considering all the shape informatio…
Predicting Shape Development: a Riemannian Method
Doğa Türkseven, Islem Rekik, Christoph von Tycowicz +1
Predicting the future development of an anatomical shape from a single baseline observation is a challenging task. But it can be essential for clinical decision-making. Research ha…
A Kendall Shape Space Approach to 3D Shape Estimation from 2D Landmarks
Martha Paskin, Daniel Baum, Mason N. Dean +1
3D shapes provide substantially more information than 2D images. However, the acquisition of 3D shapes is sometimes very difficult or even impossible in comparison with acquiring 2…