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Aaron Scherzinger
Researcher at University of Münster
Publications - 19
Citations - 111
Aaron Scherzinger is an academic researcher from University of Münster. The author has contributed to research in topics: Graph (abstract data type) & Visualization. The author has an hindex of 5, co-authored 19 publications receiving 83 citations. Previous affiliations of Aaron Scherzinger include Analysis Group.
Papers
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Journal ArticleDOI
VIPAR, a quantitative approach to 3D histopathology applied to lymphatic malformations
René Hägerling,Dominik Drees,Aaron Scherzinger,Aaron Scherzinger,Cathrin Dierkes,Silvia Martin-Almedina,Stefan Butz,Kristiana Gordon,Michael Schäfers,Klaus Hinrichs,Pia Ostergaard,Dietmar Vestweber,Tobias Goerge,Sahar Mansour,Xiaoyi Jiang,Peter S. Mortimer,Friedemann Kiefer,Friedemann Kiefer +17 more
TL;DR: VIPAR is a volume-based tissue reconstruction data extraction and analysis approach that successfully distinguished healthy from lymphedematous and lymphangiomatous skin and its application is not limited to the vascular systems or skin.
Journal ArticleDOI
Interactive Exploration of Cosmological Dark-Matter Simulation Data
Aaron Scherzinger,Tobias Brix,Dominik Drees,Andreas Volker,Kiril Radkov,Niko Santalidis,Alexander Fieguth,Klaus Hinrichs +7 more
TL;DR: This article describes a visualization tool for cosmological data resulting from dark-matter simulations that helps users explore all aspects of the data at once and receive more detailed information about structures of interest at any time.
Journal ArticleDOI
Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets
TL;DR: In this paper, the authors proposed a scalable iterative pipeline that extracts an annotated abstract graph representation from the foreground segmentation of vessel networks of arbitrary topology and vessel shape, which is controlled by a single, dimensionless, a-priori determinable parameter.
Posted Content
Barista - a Graphical Tool for Designing and Training Deep Neural Networks.
TL;DR: Barista offers a fully graphical user interface with a graph-based net topology editor and provides an end-to-end training facility for DNNs, which allows researchers to focus on solving their problems without having to write code, edit text files, or manually parse logged data.
Book ChapterDOI
SkInteract: An On-body Interaction System Based on Skin-Texture Recognition
TL;DR: This paper uses an area fingerprint sensor to capture images and locate the corresponding area within a previously created map of the skin surface, which allows to assign arbitrary semantics to areas of the user’s skin and to use the rotation as an additional input modality.