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Documents authored by Maciejewski, Ross

Document
DOI: 10.4230/OASIcs.iPMVM.2020.19
Interpolation of Scientific Image Databases

Authors: Eric Georg Kinner, Jonas Lukasczyk, David Honegger Rogers, Ross Maciejewski, and Christoph Garth

Published in: OASIcs, Volume 89, 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)

Abstract
This paper explores how recent convolutional neural network (CNN)-based techniques can be used to interpolate images inside scientific image databases. These databases are frequently used for the interactive visualization of large-scale simulations, where images correspond to samples of the parameter space (e.g., timesteps, isovalues, thresholds, etc.) and the visualization space (e.g., camera locations, clipping planes, etc.). These databases can be browsed post hoc along the sampling axis to emulate real-time interaction with large-scale datasets. However, the resulting databases are limited to their contained images, i.e., the sampling points. In this paper, we explore how efficiently and accurately CNN-based techniques can derive new images by interpolating database elements. We demonstrate on several real-world examples that the size of databases can be further reduced by dropping samples that can be interpolated post hoc with an acceptable error, which we measure qualitatively and quantitatively.
Cite as

Eric Georg Kinner, Jonas Lukasczyk, David Honegger Rogers, Ross Maciejewski, and Christoph Garth. Interpolation of Scientific Image Databases. In 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020). Open Access Series in Informatics (OASIcs), Volume 89, pp. 19:1-19:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{kinner_et_al:OASIcs.iPMVM.2020.19,
  author =	{Kinner, Eric Georg and Lukasczyk, Jonas and Rogers, David Honegger and Maciejewski, Ross and Garth, Christoph},
  title =	{{Interpolation of Scientific Image Databases}},
  booktitle =	{2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)},
  pages =	{19:1--19:17},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-183-2},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{89},
  editor =	{Garth, Christoph and Aurich, Jan C. and Linke, Barbara and M\"{u}ller, Ralf and Ravani, Bahram and Weber, Gunther H. and Kirsch, Benjamin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.iPMVM.2020.19},
  URN =		{urn:nbn:de:0030-drops-137686},
  doi =		{10.4230/OASIcs.iPMVM.2020.19},
  annote =	{Keywords: Image Interpolation, Image Database, Cinema Database}
}
Document
DOI: 10.4230/DFU.Vol2.SciViz.2011.212
Abstract Feature Space Representation for Volumetric Transfer Function Exploration

Authors: Ross Maciejewski, Yun Jang, David S. Ebert, and Kelly P. Gaither

Published in: Dagstuhl Follow-Ups, Volume 2, Scientific Visualization: Interactions, Features, Metaphors (2011)

Abstract
The application of n-dimensional transfer functions for feature segmentation has become increasingly popular in volume rendering. Recent work has focused on the utilization of higher order dimensional transfer functions incorporating spatial dimensions (x,y, and z) along with traditional feature space dimensions (value and value gradient). However, as the dimensionality increases, it becomes exceedingly difficult to abstract the transfer function into an intuitive and interactive workspace. In this work we focus on populating the traditional two-dimensional histogram with a set of derived metrics from the spatial (x, y and z) and feature space (value, value gradient, etc.) domain to create a set of abstract feature space transfer function domains. Current two-dimensional transfer function widgets typically consist of a two-dimensional histogram where each entry in the histogram represents the number of voxels that maps to that entry. In the case of an abstract transfer function design, the amount of spatial variance at that histogram coordinate is mapped instead, thereby relating additional information about the data abstraction in the projected space. Finally, a non-parametric kernel density estimation approach for feature space clustering is applied in the abstracted space, and the resultant transfer functions are discussed with respect to the space abstraction.
Cite as

Ross Maciejewski, Yun Jang, David S. Ebert, and Kelly P. Gaither. Abstract Feature Space Representation for Volumetric Transfer Function Exploration. In Scientific Visualization: Interactions, Features, Metaphors. Dagstuhl Follow-Ups, Volume 2, pp. 212-221, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@InCollection{maciejewski_et_al:DFU.Vol2.SciViz.2011.212,
  author =	{Maciejewski, Ross and Jang, Yun and Ebert, David S. and Gaither, Kelly P.},
  title =	{{Abstract Feature Space Representation for Volumetric Transfer Function Exploration}},
  booktitle =	{Scientific Visualization: Interactions, Features, Metaphors},
  pages =	{212--221},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-26-2},
  ISSN =	{1868-8977},
  year =	{2011},
  volume =	{2},
  editor =	{Hagen, Hans},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DFU.Vol2.SciViz.2011.212},
  URN =		{urn:nbn:de:0030-drops-32955},
  doi =		{10.4230/DFU.Vol2.SciViz.2011.212},
  annote =	{Keywords: Volumetric Transfer Function, Abstract Feature Space}
}
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