Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Christina Gillmann"'
Autor:
Melanie Conroy, Christina Gillmann, Francis Harvey, Tamara Mchedlidze, Sara Irina Fabrikant, Florian Windhager, Gerik Scheuermann, Timothy R. Tangherlini, Christopher N. Warren, Scott B. Weingart, Malte Rehbein, Katy Börner, Kimmo Elo, Stefan Jänicke, Andreas Kerren, Martin Nöllenburg, Tim Dwyer, Øyvind Eide, Stephen Kobourov, Gregor Betz
Publikováno v:
Frontiers in Communication, Vol 8 (2024)
Network visualization is one of the most widely used tools in digital humanities research. The idea of uncertain or “fuzzy” data is also a core notion in digital humanities research. Yet network visualizations in digital humanities do not always
Externí odkaz:
https://doaj.org/article/234933eec2bf4d85a9ebcf9e2dd91ae6
Publikováno v:
Visual Informatics, Vol 2, Iss 1, Pp 26-36 (2018)
Keyhole surgeries become increasingly important in clinical daily routine as they help minimizing the damage of a patient’s healthy tissue. The planning of keyhole surgeries is based on medical imaging and an important factor that influences the su
Externí odkaz:
https://doaj.org/article/f4b48460c5174e7f8c715313353066cc
Publikováno v:
Journal of Imaging, Vol 4, Iss 9, p 109 (2018)
Due to image reconstruction process of all image capturing methods, image data is inherently affected by uncertainty. This is caused by the underlying image reconstruction model, that is not capable to map all physical properties in its entirety. In
Externí odkaz:
https://doaj.org/article/e2bfabeefb0345c4b9420578352a21c8
Autor:
Robin G. C. Maack, Gerik Scheuermann, Hans Hagen, Jose Tiberio Hernández Peñaloza, Christina Gillmann
Publikováno v:
The Visual Computer.
In many applications, visual analytics (VA) has developed into a standard tool to ease data access and knowledge generation. VA describes a holistic cycle transforming data into hypothesis and visualization to generate insights that enhance the data.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::be4e2165062de5cd1ed36bd6fce741fd
https://doi.org/10.1007/s00371-022-02733-6
https://doi.org/10.1007/s00371-022-02733-6
Publikováno v:
Computers & Graphics. 98:293-305
Due to the limitations of existing experimental methods for capturing stereochemical molecular data, there usually is an inherent level of uncertainty present in models describing the conformation of macromolecules. This uncertainty can originate fro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4606ec1f93984e32dd3c1e9ba36e353e
https://doi.org/10.1016/j.cag.2021.05.011
https://doi.org/10.1016/j.cag.2021.05.011
Publikováno v:
Khulusi, R, Kusnick, J, Meinecke, C, Gillmann, C, Focht, J & Jänicke, S 2020, ' A Survey on Visualizations for Musical Data ', Computer Graphics Forum, vol. 39, no. 6, pp. 82-110 . https://doi.org/10.1111/cgf.13905
Digital methods are increasingly applied to store, structure and analyse vast amounts of musical data. In this context, visualization plays a crucial role, as it assists musicologists and non-expert users in data analysis and in gaining new knowledge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b50fc4ba388f09f026b2313b5c002a7
https://doi.org/10.1111/cgf.13905
https://doi.org/10.1111/cgf.13905
Autor:
Robin Georg Claus Maack, Gerik Scheuermann, Hans Hagen, Jose Tiberio Hernández Peñaloza, Christina Gillmann
In many applications, Visual Analytics(VA) has developed into a standard tool to ease data access and knowledge generation. Unfortunately, many data sources, used in the VA process, are affected by uncertainty. In addition, the VA cycle itself can in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c27dab90a4c2c9d1bfa510a4c027db59
https://doi.org/10.21203/rs.3.rs-1177485/v1
https://doi.org/10.21203/rs.3.rs-1177485/v1
Publikováno v:
2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3682adda07a7d99718d57c353c50a6ea
https://doi.org/10.1109/trex53765.2021.00014
https://doi.org/10.1109/trex53765.2021.00014
Publikováno v:
IEEE computer graphics and applications. 41(5)
A U-Net is a type of convolutional neural network that has been shown to output impressive results in medical imaging segmentation tasks. Still, neural networks in general form a black box that is hard to interpret, especially by noncomputer scientis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9601607798019640c0b9aa3ff41059b
https://pubmed.ncbi.nlm.nih.gov/34506270
https://pubmed.ncbi.nlm.nih.gov/34506270
Autor:
Noeska N. Smit, Eduard Gröller, Thomas Wischgoll, Anna Vilanova, Bernhard Preim, Theresa-Marie Rhyne, Christina Gillmann
Publikováno v:
IEEE Computer Graphics and Applications
IEEE Computer Graphics and Applications, 41(5):9535176, 7-15. IEEE Computer Society
IEEE Computer Graphics and Applications, 41(5):9535176, 7-15. IEEE Computer Society
The medical domain has been an inspiring application area in visualization research for many years already, but many open challenges remain. The driving forces of medical visualization research have been strengthened by novel developments, for exampl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::38911a9bfbaa3394205c2d9111fe93d2
https://pubmed.ncbi.nlm.nih.gov/34506269
https://pubmed.ncbi.nlm.nih.gov/34506269