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Algebraic design of multi-dimensional transfer function using transfer function synthesizer

JOURNAL OF visualization 2017年第1期20卷 151-162页

作者： Kawamura, Takuma Idomura, Yasuhiro Miyamura, Hiroko Takemiya, Hiroshi Japan Atom Energy Agcy Ctr Computat Sci & eSyst 178-4 Wakashiba Kashiwa Chiba 2770871 Japan

In this paper, we propose a novel transfer function (TF) design interface for multi-variate volume rendering. To design higher dimensional TFs in an general and flexible manner, a transfer function synthesizer (TFS) is developed. On the TFS, multi-dimensional TFs are generated by algebraic synthesis of one-dimensional TFs, which are designed based on the conventional GUIs or algebraic expressions. The TFS enables not only multi-variate volume rendering, but also general visualization techniques, such as surface visualization and image composition, within the framework of volume rendering. The TFS is implemented on the remote visualization system PBVR, and applied to various multi-variate scalar volume data generated from nuclear applications.

关键词： Volume rendering multi-variate visualization Large-scale data

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Evaluation of Trend Localization with multi-variate visualizations

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2011年第12期17卷 2053-2062页

作者： Livingston, Mark A. Decker, Jonathan W. USN Res Lab Monterey CA 93943 USA

multi-valued data sets are increasingly common, with the number of dimensions growing. A number of multi-variate visualization techniques have been presented to display such data. However, evaluating the utility of such techniques for general data sets remains difficult. Thus most techniques are studied on only one data set. Another criticism that could be levied against previous evaluations of multi-variate visualizations is that the task doesn't require the presence of multiple variables. At the same time, the taxonomy of tasks that users may perform visually is extensive. We designed a task, trend localization, that required comparison of multiple data values in a multi-variate visualization. We then conducted a user study with this task, evaluating five multi-variate visualization techniques from the literature (Brush Strokes, Data-Driven Spots, Oriented Slivers, Color Blending, Dimensional Stacking) and juxtaposed grayscale maps. We report the results and discuss the implications for both the techniques and the task.

关键词： User study multi-variate visualization visual task design visual analytics

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An Evaluation of Methods for Encoding multiple, 2D Spatial Data

An Evaluation of Methods for Encoding Multiple, 2D Spatial D...

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Conference on visualization and Data Analysis 2011

作者： Livingston, Mark A. Decker, Jonathan Ai, Zhuming USN Res Lab Washington DC 20375 USA

ISBN: (纸本)9780819484055

Datasets over a spatial domain are common in a number of fields, often with multiple layers (or variables) within data that must be understood together via spatial locality. Thus one area of long-standing interest is increasing the number of variables encoded by properties of the visualization. A number of properties have been demonstrated and/or proven successful with specific tasks or data, but there has been relatively little work comparing the utility of diverse techniques for multi-layer visualization. As part of our efforts to evaluate the applicability of such visualizations, we implemented five techniques which represent a broad range of existing research (Color Blending, Oriented Slivers, Data-Driven Spots, Brush Strokes, and Stick Figures). Then we conducted a user study wherein subjects were presented with composites of three, four, and five layers (variables) using one of these methods and asked to perform a task common to our intended end users (GIS analysts). We found that the Oriented Slivers and Data-Driven Spots performed the best, with Stick Figures yielding the lowest accuracy. Through analyzing our data, we hope to gain insight into which techniques merit further exploration and offer promise for visualization of data sets with ever-increasing size.

关键词： multi-variate visualization scalar fields spatial surfaces glyphs color encoding user study

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Continuous Parallel Coordinates

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2009年第6期15卷 1531-1538页

作者： Heinrich, Julian Weiskopf, Daniel Univ Stuttgart VISUS Visualizat Res Ctr D-70569 Stuttgart Germany

Typical scientific data is represented on a grid with appropriate interpolation or approximation schemes, defined on a continuous domain. The visualization of such data in parallel coordinates may reveal patterns latently contained in the data and thus can improve the understanding of multidimensional relations. In this paper, we adopt the concept of continuous scatterplots for the visualization of spatially continuous input data to derive a density model for parallel coordinates. Based on the point-line duality between scatterplots and parallel coordinates, we propose a mathematical model that maps density from a continuous scatterplot to parallel coordinates and present different algorithms for both numerical and analytical computation of the resulting density field. In addition, we show how the 2-D model can be used to successively construct continuous parallel coordinates with an arbitrary number of dimensions. Since continuous parallel coordinates interpolate data values within grid cells, a scalable and dense visualization is achieved, which will be demonstrated for typical multi-variate scientific data.

关键词： Parallel coordinates integrating spatial and non-spatial data visualization multi-variate visualization interpolation

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Continuous Parallel Coordinates

Continuous Parallel Coordinates

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IEEE Information visualization Conference/IEEE visualization Conference

作者： Heinrich, Julian Weiskopf, Daniel Univ Stuttgart VISUS Visualizat Res Ctr D-70569 Stuttgart Germany

关键词： Parallel coordinates integrating spatial and non-spatial data visualization multi-variate visualization interpolation

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Scalable data servers for large multivariate volume visualization

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2006年第5期12卷 1291-1298页

作者： Glatter, Markus Mollenhour, Colin Huang, Jian Gao, Jinzhu Univ Tennessee Knoxville TN 37996 USA Oak Ridge Natl Lab Oak Ridge TN USA

Volumetric datasets with multiple variables on each voxel over multiple time steps are often complex, especially when considering the exponentially large attribute space formed by the variables in combination with the spatial and temporal dimensions. It is intuitive, practical, and thus often desirable, to interactively select a subset of the data from within that high-dimensional value space for efficient visualization. This approach is straightforward to implement if the dataset is small enough to be stored entirely in-core. However, to handle datasets sized at hundreds of gigabytes and beyond, this simplistic approach becomes infeasible and thus, more sophisticated solutions are needed. In this work, we developed a system that supports efficient visualization of an arbitrary subset, selected by range-queries, of a large multivariate time-varying dataset. By employing specialized data structures and schemes of data distribution, our system can leverage a large number of networked computers as parallel data servers, and guarantees a near optimal load-balance. We demonstrate our system of scalable data servers using two large time-varying simulation datasets.

关键词： parallel and distributed volume visualization large data set visualization multi-variate visualization volume visualization

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Scalable data servers for large multivariate volume visualization

Scalable data servers for large multivariate volume visualiz...

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IEEE visualization Conference (Vis 2006)/IEEE Symposium on Information visualization (InfoVis 2006)

作者： Glatter, Markus Mollenhour, Colin Huang, Jian Gao, Jinzhu Univ Tennessee Knoxville TN 37996 USA Oak Ridge Natl Lab Oak Ridge TN USA

关键词： parallel and distributed volume visualization large data set visualization multi-variate visualization volume visualization

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Scout: A hardware-accelerated system for quantitatively driven visualization and analysis 04

Scout: A hardware-accelerated system for quantitatively driv...

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IEEE visualization 2004 Conference

作者： McCormick, PS Inman, J Ahrens, JP Hansen, C Roth, G Los Alamos Natl Lab Adv Comp Lab Los Alamos NM 87545 USA

ISBN: (纸本)0780387880

Quantitative techniques for visualization are critical to the successful analysis of both acquired and simulated scientific data. Many visualization techniques rely on indirect mappings, such as transfer functions, to produce the final imagery. In many situations, it is preferable and more powerful to express these mappings as mathematical expressions, or queries, that can then be directly applied to the data. In this paper, we present a hardware-accelerated system that provides such capabilites and exploits current graphics hardware for portions of the computational tasks that would otherwise be executed on the CPU. In our approach, the direct programming of the graphics processor using a concise data parallel language, gives scientists the capability to efficiently explore and visualize data sets.

关键词： visualization systems hardware acceleration multi-variate visualization volume rendering

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