Feature detection in linked derived spaces

Henze,

doi:10.1109/visual.1998.745289

Cited by 28 publications

(22 citation statements)

References 12 publications

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“…However, there are some interesting correspondences. For example, featurebased approaches in the scivis literature often involve nontrivial decisions at both the abstraction and visual encoding stages [18], and transfer function design [21] also falls into the visual encoding stage.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

A Nested Model for Visualization Design and Validation

Munzner

2009

IEEE Trans. Visual. Comput. Graphics

732

636

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Abstract-We present a nested model for the visualization design and validation with four layers: characterize the task and data in the vocabulary of the problem domain, abstract into operations and data types, design visual encoding and interaction techniques, and create algorithms to execute techniques efficiently. The output from a level above is input to the level below, bringing attention to the design challenge that an upstream error inevitably cascades to all downstream levels. This model provides prescriptive guidance for determining appropriate evaluation approaches by identifying threats to validity unique to each level. We also provide three recommendations motivated by this model: authors should distinguish between these levels when claiming contributions at more than one of them, authors should explicitly state upstream assumptions at levels above the focus of a paper, and visualization venues should accept more papers on domain characterization.

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Section: Discussion and Limitationsmentioning

confidence: 99%

A Nested Model for Visualization Design and Validation

Munzner

2009

IEEE Trans. Visual. Comput. Graphics

732

636

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show abstract

“…Here, σ 2 is the square of the rate-of-strain tensor and ω 2 is the square of the vorticity tensor (Panton 1984;Douady et al 1991). The Q-criterion is measure of the relative contributions of strain and rotation on fluid elements in the flow and has been used in several studies to identify regions in the flow where rotation dominates (Q < 0) and where strain dominates (Q > 0) (Horne et al 1992;Henze 1998;Lapeyre et al 1999;Haller 2001). Different types of mixing have been reported in the different regions fluid flow (Weiss 1991;Boratav et al 1998;Provenzale 1999;Haller and Yuan 2000).…”

Section: Mixing and The Sgs Growth Ratementioning

confidence: 99%

Effects of Turbulent Fluctuations on Nanoparticle Coagulation in Shear Flows

Garrick

2011

Aerosol Science and Technology

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The effects of fluid turbulence on the coagulation of aerosols are studied quantitatively and qualitatively. Direct numerical simulation data is used to isolate the effect of the small or subgrid-scale (SGS) particle-particle interactions on nanoparticle coagulation in three-dimensional flows. The rate of particle growth is decomposed into the contribution of the large-scales and small-scales interactions. The contribution of the small-scale interactions is presented as a function of time, space, flow dynamics, and coagulation Damköhler number. Results show that small-scale interactions act to both increase and decrease particle growth. The probability density functions (PDFs) of the SGS growth rate exhibit a negative bias, which increases with time and coagulation Damköhler number. Additionally, PDFs conditioned on the Q-criterion suggest that the contribution of the small-scale interactions primarily act to reduce particle growth in regions characterized by fluid rotation.

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“…In the WEAVE system, a combination of physical and information visualization views is used for exploration of cardiac simulation and measurement data [9]. Henze [12] developed a multiple-view-based system for exploration of time-varying computational fluid dynamics data. Advanced queries are supported by this system via selection of data subsets in each view (here termed portraits).…”

Section: Related Workmentioning

confidence: 99%

Linking Advanced Visualization and MATLAB for the Analysis of 3D Gene Expression Data

Rübel

Keränen²,

Biggin³

et al. 2012

Mathematics and Visualization

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Summary. Three-dimensional gene expression PointCloud data generated by the Berkeley Drosophila Transcription Network Project (BDTNP) provides quantitative information about the spatial and temporal expression of genes in early Drosophila embryos at cellular resolution. The BDTNP team visualizes and analyzes PointCloud data using the software application PointCloudXplore (PCX). To maximize the impact of novel, complex data sets, such as PointClouds, the data needs to be accessible to biologists and comprehensible to developers of analysis functions. We address this challenge by linking PCX and Matlab R 6 via a dedicated interface, thereby providing biologists seamless access to advanced data analysis functions and giving bioinformatics researchers the opportunity to integrate their analysis directly into the visualization application. To demonstrate the usefulness of this approach, we computationally model parts of the expression pattern of the gene even skipped using a genetic algorithm implemented in Matlab and integrated into PCX via our Matlab interface.

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Feature detection in linked derived spaces

Cited by 28 publications

References 12 publications

A Nested Model for Visualization Design and Validation

A Nested Model for Visualization Design and Validation

Effects of Turbulent Fluctuations on Nanoparticle Coagulation in Shear Flows

Linking Advanced Visualization and MATLAB for the Analysis of 3D Gene Expression Data

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