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Displaying 1-9 out of 9 total
Edge Groups: An Approach to Understanding the Mesh Quality of Marching Methods
Found in: IEEE Transactions on Visualization and Computer Graphics
By carlos dietrich, Carlos Scheidegger, João Comba, Luciana Nedel, Cláudio Silva
Issue Date:November 2008
pp. 1651-1666
Marching Cubes is the most popular isosurface extraction algorithm due to its simplicity, efficiency and robustness. It has been widely studied, improved, and extended. While much early work was concerned with efficiency and correctness issues, lately ther...
 
Nanocubes for Real-Time Exploration of Spatiotemporal Datasets
Found in: IEEE Transactions on Visualization and Computer Graphics
By Lauro Lins,James T. Klosowski,Carlos Scheidegger
Issue Date:December 2013
pp. 2456-2465
Consider real-time exploration of large multidimensional spatiotemporal datasets with billions of entries, each defined by a location, a time, and other attributes. Are certain attributes correlated spatially or temporally? Are there trends or outliers in ...
 
High-Quality Extraction of Isosurfaces from Regular and Irregular Grids
Found in: IEEE Transactions on Visualization and Computer Graphics
By John Schreiner,Carlos Scheidegger,Claudio Silva
Issue Date:September 2006
pp. 1205-1212
Isosurfaces are ubiquitous in many fields, including visualization, graphics, and vision. They are often the main computational component of important processing pipelines (e.g. , surface reconstruction), and are heavily used in practice. The classical app...
 
Verifying Volume Rendering Using Discretization Error Analysis
Found in: IEEE Transactions on Visualization and Computer Graphics
By Tiago Etiene,Daniel Jonsson,Timo Ropinski,Carlos Scheidegger,Joao L. D. Comba,Luis Gustavo Nonato,Robert M. Kirby,Anders Ynnerman,Claudio T. Silva
Issue Date:January 2014
pp. 140-154
We propose an approach for verification of volume rendering correctness based on an analysis of the volume rendering integral, the basis of most DVR algorithms. With respect to the most common discretization of this continuous model (Riemann summation), we...
 
Multilevel agglomerative edge bundling for visualizing large graphs
Found in: 2011 IEEE Pacific Visualization Symposium (PacificVis)
By Emden R Gansner, Yifan Hu,Stephen North,Carlos Scheidegger
Issue Date:March 2011
pp. 187-194
Graphs are often used to encapsulate relationships between objects. Node-link diagrams, commonly used to visualize graphs, suffer from visual clutter on large graphs. Edge bundling is an effective technique for alleviating clutter and revealing high-level ...
 
A Report From VisWeek 2010
Found in: Computing in Science and Engineering
By Carlos Scheidegger, Cláudio T. Silva, Daniel Weiskopf
Issue Date:March 2011
pp. 70-77
<p>VisWeek 2010 covered the latest visualization and data analysis research and offered an excellent forum for discussing new trends in these fields.</p>
 
Verifiable Visualization for Isosurface Extraction
Found in: IEEE Transactions on Visualization and Computer Graphics
By Tiago Etiene, Carlos Scheidegger, Luis Gustavo Nonato, Robert Mike Kirby, Cláudio Silva
Issue Date:November 2009
pp. 1227-1234
Visual representations of isosurfaces are ubiquitous in the scientific and engineering literature. In this paper, we present techniques to assess the behavior of isosurface extraction codes. Where applicable, these techniques allow us to distinguish whethe...
 
End-to-End eScience: Integrating Workflow, Query, Visualization, and Provenance at an Ocean Observatory
Found in: eScience, IEEE International Conference on
By Bill Howe, Peter Lawson, Renee Bellinger, Erik Anderson, Emanuele Santos, Juliana Freire, Carlos Scheidegger, António Baptista, Cláudio Silva
Issue Date:December 2008
pp. 127-134
Data analysis tasks at an Ocean Observatory require integrative and and domain-specialized use of database, workflow, visualization systems.
 
Nanocubes for Real-Time Exploration of Spatiotemporal Datasets
Found in: IEEE Transactions on Visualization and Computer Graphics
By Lauro Lins,James T. Klosowski,Carlos Scheidegger
Issue Date:December 2013
pp. 2456-2465
Consider real-time exploration of large multidimensional spatiotemporal datasets with billions of entries, each defined by a location, a time, and other attributes. Are certain attributes correlated spatially or temporally? Are there trends or outliers in ...
 
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