18 citations · 35 across the 3 of their papers we have counts for
7 papers
Unsupervised Features Learning for Sampled Vector Fields
Mateusz Juda
In this paper we introduce a new approach to computing hidden features of sampled vector fields. The basic idea is to convert the vector field data to a graph structure and use too…
Persistence Bag-of-Words for Topological Data Analysis
Bartosz Zieliński, Michał Lipiński, Mateusz Juda +2
Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs). PDs exhibit, however, complex struct…
Persistence Codebooks for Topological Data Analysis
Bartosz Zielinski, Michal Lipinski, Mateusz Juda +2
Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs) which are 2D multisets of points. The…
Persistent Homology of Morse Decompositions in Combinatorial Dynamics
Tamal K. Dey, Mateusz Juda, Tomasz Kapela +3
We investigate combinatorial dynamical systems on simplicial complexes considered as {\em finite topological spaces}. Such systems arise in a natural way from sampling dynamics and…
A Study on Topological Descriptors for the Analysis of 3D Surface Texture
Matthias Zeppelzauer, Bartosz Zielinski, Mateusz Juda +1
Methods from computational topology are becoming more and more popular in computer vision and have shown to improve the state-of-the-art in several tasks. In this paper, we investi…
Topological descriptors for 3D surface analysis
Matthias Zeppelzauer, Bartosz Zieliński, Mateusz Juda +1
We investigate topological descriptors for 3D surface analysis, i.e. the classification of surfaces according to their geometric fine structure. On a dataset of high-resolution 3D…