activity
20162020
collaborators

6 papers

cs.CV2020

Visual Probing and Correction of Object Recognition Models with Interactive user feedback

Viny Saajan Victor, Pramod Vadiraja, Jan-Tobias Sohns +1

With the advent of state-of-the-art machine learning and deep learning technologies, several industries are moving towards the field. Applications of such technologies are highly d…

math.AT2019

Topological Machine Learning with Persistence Indicator Functions

Bastian Rieck, Filip Sadlo, Heike Leitte

Techniques from computational topology, in particular persistent homology, are becoming increasingly relevant for data analysis. Their stable metrics permit the use of many distanc…

math.AT2019

Hierarchies and Ranks for Persistence Pairs

Bastian Rieck, Filip Sadlo, Heike Leitte

We develop a novel hierarchy for zero-dimensional persistence pairs, i.e., connected components, which is capable of capturing more fine-grained spatial relations between persisten…

math.AT2019

Persistence Concepts for 2D Skeleton Evolution Analysis

Bastian Rieck, Filip Sadlo, Heike Leitte

In this work, we present concepts for the analysis of the evolution of two-dimensional skeletons. By introducing novel persistence concepts, we are able to reduce typical temporal…

math.AT2019

Persistent Intersection Homology for the Analysis of Discrete Data

Bastian Rieck, Markus Banagl, Filip Sadlo +1

Topological data analysis is becoming increasingly relevant to support the analysis of unstructured data sets. A common assumption in data analysis is that the data set is a sample…

cs.CG2016

Feature Extraction from Segmentations of Neuromuscular Junctions

Julia Portl, Heike Leitte

Segmentations are often necessary for the analysis of image data. They are used to identify different objects, for example cell nuclei, mitochondria, or complete cells in microscop…