17 citations · 21 across the 9 of their papers we have counts for
9 papers
Learning the Geometry of Data: A Mathematical Review of Shape Space Analysis
Gary P. T. Choi, Khanh Dao Duc, Shira Faigenbaum-Golovin +6
A central objective of machine learning is to identify structure and patterns in data. Advances in data acquisition have increasingly produced datasets whose observations possess r…
VeloTree: Inferring single-cell trajectories from RNA velocity fields with varifold distances
Elodie Maignant, Tim Conrad, Christoph von Tycowicz
Trajectory inference is a critical problem in single-cell transcriptomics, which aims to reconstruct the dynamic process underlying a population of cells from sequencing data. Of p…
Tree inference with varifold distances
Elodie Maignant, Tim Conrad, Christoph von Tycowicz
In this paper, we consider a tree inference problem motivated by the critical problem in single-cell genomics of reconstructing dynamic cellular processes from sequencing data. In…
Bi-invariant Geodesic Regression with Data from the Osteoarthritis Initiative
Johannes Schade, Christoph von Tycowicz, Martin Hanik
Many phenomena are naturally characterized by measuring continuous transformations such as shape changes in medicine or articulated systems in robotics. Modeling the variability in…
Sasaki Metric for Spline Models of Manifold-Valued Trajectories
Esfandiar Nava-Yazdani, Felix Ambellan, Martin Hanik +1
We propose a generic spatiotemporal framework to analyze manifold-valued measurements, which allows for employing an intrinsic and computationally efficient Riemannian hierarchical…
SHREC 2022 Track on Online Detection of Heterogeneous Gestures
Ariel Caputo, Marco Emporio, Andrea Giachetti +10
This paper presents the outcomes of a contest organized to evaluate methods for the online recognition of heterogeneous gestures from sequences of 3D hand poses. The task is the de…
A Nonlinear Hierarchical Model for Longitudinal Data on Manifolds
Martin Hanik, Hans-Christian Hege, Christoph von Tycowicz
Large longitudinal studies provide lots of valuable information, especially in medical applications. A problem which must be taken care of in order to utilize their full potential…
Towards Shape-based Knee Osteoarthritis Classification using Graph Convolutional Networks
Christoph von Tycowicz
We present a transductive learning approach for morphometric osteophyte grading based on geometric deep learning. We formulate the grading task as semi-supervised node classificati…
Geodesic analysis in Kendall's shape space with epidemiological applications
Esfandiar Nava-Yazdani, Hans-Christian Hege, T. J. Sullivan +1
We analytically determine Jacobi fields and parallel transports and compute geodesic regression in Kendall's shape space. Using the derived expressions, we can fully leverage the g…