Brian Summa
Tulane University
44 Papers
147 Citations
Brian Summa is an academic researcher from Tulane University. The author has contributed to research in topics: Computer science & Visualization. The author has an hindex of 12, co-authored 33 publications. Previous affiliations of Brian Summa include Scientific Computing and Imaging Institute & University of Utah.
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Papers
Panorama weaving: fast and flexible seam processing
Brian Summa,Julien Tierny,Valerio Pascucci +2 more
- 01 Jul 2012
TL;DR: This paper introduces the Panorama Weaving technique for seam creation and editing in an image mosaic and provides a procedure to create boundaries for panoramas that is fast, has low memory requirements and is easy to parallelize.
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PIDX: Efficient Parallel I/O for Multi-resolution Multi-dimensional Scientific Datasets
Sidharth Kumar,Venkatram Vishwanath,Philip Carns,Brian Summa,Giorgio Scorzelli,Valerio Pascucci,Robert Ross,Jackie Chen,Hemanth Kolla,Ray Grout +9 more
- 26 Sep 2011
TL;DR: An overview of the IDX file format and how it is generated using PIDX is provided, and a data model description and a novel aggregation strategy to enhance the scalability of the PIDX library are presented.
Interactive editing of massive imagery made simple: Turning Atlanta into Atlantis
TL;DR: A progressive Poisson solver is introduced that processes images in a purely coarse-to-fine manner, providing near instantaneous global approximations for interactive display and providing a robust and scalable out-of-core solver that consistently offers high-quality solutions while maintaining strict control over system resources.
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Multiscale 3-dimensional pathology findings of COVID-19 diseased lung using high-resolution cleared tissue microscopy
Guang Li,Sharon E. Fox,Brian Summa,Bihe Hu,Carola Wenk,Aibek Akmatbekov,Jack L. Harbert,Richard S. Vander Heide,J. Quincy Brown +8 more
TL;DR: The first 3-dimensional images of lung autopsy tissues taken from a COVID-19 patient are presented, including 3D “virtual histology” of cubic-millimeter volumes of the diseased lung, providing unique insights into disease processes contributing to mortality that could inform frontline treatment decisions.
•Posted Content
Persistence Atlas for Critical Point Variability in Ensembles
TL;DR: A new approach for the visualization and analysis of the spatial variability of features of interest represented by critical points in ensemble data, based on the new notion of Persistence Map, a measure of the geometrical density in critical points which leverages the robustness to noise of topological persistence to better emphasize salient features.
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