10 citations · 20 across the 4 of their papers we have counts for
6 papers
A Stable Multi-Scale Kernel for Topological Machine Learning
Jan Reininghaus, Stefan Huber, Ulrich Bauer +1
Topological data analysis offers a rich source of valuable information to study vision problems. Yet, so far we lack a theoretically sound connection to popular kernel-based learni…
Acceleration feature points of unsteady shear flows
Jens Kasten, Jan Reininghaus, Ingrid Hotz +4
In this paper, we propose a novel framework to extract features such as vortex cores and saddle points in two-dimensional unsteady flows. This feature extraction strategy generaliz…
Distributed computation of persistent homology
Ulrich Bauer, Michael Kerber, Jan Reininghaus
Persistent homology is a popular and powerful tool for capturing topological features of data. Advances in algorithms for computing persistent homology have reduced the computation…
Clear and Compress: Computing Persistent Homology in Chunks
Ulrich Bauer, Michael Kerber, Jan Reininghaus
We present a parallelizable algorithm for computing the persistent homology of a filtered chain complex. Our approach differs from the commonly used reduction algorithm by first co…
Combinatorial Gradient Fields for 2D Images with Empirically Convergent Separatrices
Jan Reininghaus, David Günther, Ingrid Hotz +2
This paper proposes an efficient probabilistic method that computes combinatorial gradient fields for two dimensional image data. In contrast to existing algorithms, this approach…
SHREC 2011: robust feature detection and description benchmark
E. Boyer, A. M. Bronstein, M. M. Bronstein +15
Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. SHREC'11 r…