GuideME: Slice‐guided Semiautomatic Multivariate Exploration of Volumes

Zhou, Liang; Hansen, Charles

doi:10.1111/cgf.12371

Cited by 15 publications

(18 citation statements)

References 24 publications

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“…Such a blue-white-red colormap is typically used to localize regions with extreme scalar magnitudes in the petroleum industry. Zhou and Hansen [114] apply a ColorBrewer’s sequential color-map to show the confidence of feature boundaries of one or more attributes of volumetric datasets on 2D slices and allow the user to crop the colormap to highlight values of interest. Comparing and identifying computed confidence of different regions on feature boundaries are the tasks associated with the colormap.…”

Section: Colormap Applicationsmentioning

confidence: 99%

“…In all 24 works adopting existing colormaps, 16 papers ([1], [2], [7], [8], [14], [17], [25], [38], [39], [53], [59], [87], [99], [102], [114]) apply colormaps from the ColorBrewer tool [44], three papers ([49], [54], [63]) utilize the colormap designed by Moreland [73], four papers ([1], [38], [63], [68]) use the black body or heat colormap, i.e., the black to red to yellow or white colormap, one paper [5] adopts the isoluminant multihue colormap proposed by Kindlmann et al [55] and five papers utilize the rainbow colormap. For the 18 papers with originally designed colormaps, colormaps from [13], [101] are similar to the ColorBrewer colormaps, whereas [6], [47], [59], [69] utilize luminance increasing multihue colormaps, and [26], [29], [37], [54], [97] design multihue colormaps with the issues of standard rainbow colormap in mind.…”

Section: Colormap Applicationsmentioning

confidence: 99%

“…Regardless, authors of these works are fully aware of the issues of rainbow colormaps and cite the rainbow colormap paper by Borland and Taylor [16]. Eleven papers ([1], [2], [8], [14], [29], [51], [63], [68], [98], [99], [110], [114]) allow users to edit colormaps to explore or highlight regions of interest in their data. Five papers ([6], [47], [51], [63], [68]) utilize colormaps specifically tailored to highlight small features in data.…”

Section: Colormap Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Colormaps in Visualization

Zhou

Hansen

2016

IEEE Trans. Visual. Comput. Graphics

112

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Colormaps are a vital method for users to gain insights into data in a visualization. With a good choice of colormaps, users are able to acquire information in the data more effectively and efficiently. In this survey, we attempt to provide readers with a comprehensive review of colormap generation techniques and provide readers a taxonomy which is helpful for finding appropriate techniques to use for their data and applications. Specifically, we first briefly introduce the basics of color spaces including color appearance models. In the core of our paper, we survey colormap generation techniques, including the latest advances in the field by grouping these techniques into four classes: procedural methods, user-study based methods, rule-based methods, and data-driven methods; we also include a section on methods that are beyond pure data comprehension purposes. We then classify colormapping techniques into a taxonomy for readers to quickly identify the appropriate techniques they might use. Furthermore, a representative set of visualization techniques that explicitly discuss the use of colormaps is reviewed and classified based on the nature of the data in these applications. Our paper is also intended to be a reference of colormap choices for readers when they are faced with similar data and/or tasks.

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Section: Colormap Applicationsmentioning

confidence: 99%

Section: Colormap Applicationsmentioning

confidence: 99%

Section: Colormap Applicationsmentioning

confidence: 99%

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A Survey of Colormaps in Visualization

Zhou

Hansen

2016

IEEE Trans. Visual. Comput. Graphics

112

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“…Seismic data is another domain with specific attributes that constitutes many dimensions and thus requires advanced user interfaces to control. Zhou and Hansen [ZH13,ZH14] present examples of this, which are discussed in Section 8.4.1.…”

Section: Domain Specific Aggregationmentioning

confidence: 99%

“…The system also supports parallel coordinates manipulation. A similar approach has been taken by Zhou and Hansen [ZH14], where advanced selections for more complex areas of interest on slices of multivariate data are performed using a lasso tool that automatically snaps to boundaries. Based on the multivariate attribute space, the selection defines a feature for which a TF definition is automatically generated.…”

Section: Slice View Interactionmentioning

confidence: 99%

State of the Art in Transfer Functions for Direct Volume Rendering

Ljung

Krüger

Gröller

et al. 2016

Computer Graphics Forum

103

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A central topic in scientific visualization is the transfer function (TF) for volume rendering. The TF serves a fundamental role in translating scalar and multivariate data into color and opacity to express and reveal the relevant features present in the data studied. Beyond this core functionality, TFs also serve as a tool for encoding and utilizing domain knowledge and as an expression for visual design of material appearances. TFs also enable interactive volumetric exploration of complex data. The purpose of this state-of-the-art report (STAR) is to provide an overview of research into the various aspects of TFs, which lead to interpretation of the underlying data through the use of meaningful visual representations. The STAR classifies TF research into the following aspects: dimensionality, derived attributes, aggregated attributes, rendering aspects, automation, and user interfaces. The STAR concludes with some interesting research challenges that form the basis of an agenda for the development of next generation TF tools and methodologies.

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Domain‐specific visualization system based on automatic multiseed recommendations: Extracting stratigraphic structures

Liu

2019

Softw Pract Exp

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SummaryUnderground flow path (UFP) is one of the most significant stratigraphic structures in revealing the distribution of oil or gas from seismic data. We design a domain‐specific visualization system to extract the stratigraphic structures by seed point tracing and explore the seismic data by graph interactions. The seeds are automatically generated by kernel function–based density gradients computation. Users are allowed to adjust the recommended seeds by fine‐tuning them with visual interactions. The seeds are further merged by a weighted quick‐union algorithm to get the link information to construct a graph. Different types of nodes in the graph are designed to enable users to explore the extracted UFP structures intuitively. Finally, we evaluated the proposed approach by performance tests, sensitivity tests, and ground truth tests. The feedback from the domain experts demonstrates that the proposed visualization tool improved the capability of revealing the distribution and geostructures of UFPs compared with the existing methods.

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GuideME: Slice‐guided Semiautomatic Multivariate Exploration of Volumes

Cited by 15 publications

References 24 publications

A Survey of Colormaps in Visualization

A Survey of Colormaps in Visualization

State of the Art in Transfer Functions for Direct Volume Rendering

Domain‐specific visualization system based on automatic multiseed recommendations: Extracting stratigraphic structures

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