Refine
Year of publication
Document Type
- Article (24)
- In Proceedings (19)
- ZIB-Report (11)
- Book chapter (4)
- Software (2)
- Doctoral Thesis (1)
- In Collection (1)
- Research data (1)
Language
- English (63) (remove)
Keywords
- Statistical shape analysis (3)
- Classification (2)
- Lie groups (2)
- Manifold valued statistics (2)
- Geometric statistics (1)
- Hierarchical (1)
- Kendall (1)
- Longitudinal (1)
- Osteoarthritis (1)
- Principal geodesic analysis (1)
- Riemannian manifolds (1)
- Shape Space (1)
- Tangent principal component analysis (1)
- compression (1)
- geometric morphometrics (1)
- geometric statistics (1)
- mesh compression (1)
- optimal control (1)
- shape analysis (1)
- shape space, shape trajectories, geodesic regression, longitudinal analysis, osteoarthritis (1)
- trajectory storage (1)
Institute
- Visual and Data-centric Computing (60)
- Visual Data Analysis (44)
- Geometric Data Analysis and Processing (15)
- Therapy Planning (11)
- Numerical Mathematics (5)
- Computational Systems Biology (3)
- Visual Data Analysis in Science and Engineering (3)
- Computational Medicine (2)
- Modeling and Simulation of Complex Processes (2)
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 scientific computing, and discrete geometry to name but a few. With increasing interest in nonlinear data science, its constructive approach has been shown to provide a principled way to generalize parametric smooth curves to manifolds. These curves have found remarkable new applications in the analysis of parameter-dependent, geometric data. This article provides a survey of the recent theoretical developments in this exciting area as well as its applications in fields such as geometric morphometrics and longitudinal data analysis in medicine, archaeology, and meteorology.
Gesture recognition is a tool to enable novel interactions with different techniques and
applications, like Mixed Reality and Virtual Reality environments. With all the recent
advancements in gesture recognition from skeletal data, it is still unclear how well state-of-
the-art techniques perform in a scenario using precise motions with two hands. This
paper presents the results of the SHREC 2024 contest organized to evaluate methods
for their recognition of highly similar hand motions using the skeletal spatial coordinate
data of both hands. The task is the recognition of 7 motion classes given their spatial
coordinates in a frame-by-frame motion. The skeletal data has been captured using
a Vicon system and pre-processed into a coordinate system using Blender and Vicon
Shogun Post. We created a small, novel dataset with a high variety of durations in
frames. This paper shows the results of the contest, showing the techniques created
by the 5 research groups on this challenging task and comparing them to our baseline
method.