Visualization Interfaces

Project Mission

With the increasing power of graphics hardware and the maturity of rendering algorithms, the ability to generate useful visualizations is now more often limited by human interaction with the system. Sophisticated visualization tasks and algorithms require the mastery of the details of the algorithm, the properties of the data, and the capabilities of the hardware. These hurdles often discourage those most knowledgeable of the underlying problem from driving the visual exploration process. As a result, the potential of the visualization is limited and the extent of scientific discovery may be reduced.

Our user interface research addresses this urgent need for innovation for demanding data visualization tasks.

 
 
User Interfaces
Many new data reduction and advanced rendering methods have been invented for visualizing large, complex time-varying volume data sets. An equally important but often neglected aspect of a visualization solution is the accompanying interface through which the user makes, view, and manipulate visualizations. A carefully designed interface can make the exploration of large, complex data an easier job. The interface should abstract the complexity of visualization algorithms from the user, and display information in different but tightly coupled spaces to facilitate analysis and enable discovery.
 
 
 
 
Visualizing Visualizations
As both the scale and complexity of data analysis tasks continue to increase, various information about data exploration should be shared and reused to leverage the knowledge and experience scientists gain from data visualization. Essentially, we need to coherently manage, represent, and share information about both the visualization process and results (images and insights). Our approach to this problem is to encapsulate all information with a powerful visual interface that can help scientists keep track of their visualization experience and findings, use it to generate new visualizations, and share it with others. The concept of "visualizing visualizations" is introduced which could revolutionize the traditional paradigm for data visualization.
 
 
 
 
Spreadsheet Interfaces
A spreadsheet interface presents the user visualization results organized in a tabular fashion. Data exploration becomes slicing through a multidimensional parameters space. Visualizations are refined by The user can operate on a whole row or column of the current table to refine visualizations.
 
 
A Model for the Visualization Exploration Process
T.J. Jankun-Kelly, Kwan-Liu Ma, and Michael Gertz
In Proceedings of IEEE Visualization 2002 Conference
October, 2002, pp. 323-330
The current state of the art in visualization research places a strong emphasis on different techniques to derive insight from disparate types of data. However, little work has investigated the visualization process itself. The information content of the visualization process—the results, history, and relationships between those results—is addressed by this work. A characterization of the visualization process is discussed, leading to a general model of the visualization exploration process ...
 
Visualization Exploration and Encapsulation Via a Spreadsheet-Like Interface
T.J. Jankun-Kelly and Kwan-Liu Ma
IEEE Transactions on Visualization and Computer Graphics
Volume 7, Number 3, July, 2001, pp. 275-287
Exploring complex, very large data sets requires interfaces to present and navigate through the visualization of the data. Two types of audience benefit from such coherent organization and representation: first, the user of the visualization system can examine and evaluate their data more efficiently; second, collaborators or reviewers can quickly understand and extend the visualization. The needs of these two groups are addressed by the spreadsheet-like interface described here ...
 
A Spreadsheet Interface for Visualization Exploration
T.J. Jankun-Kelly and Kwan-Liu Ma
In Proceedings of IEEE Visualization 2000 Conference
October, 2000, pp. 69-76
As the size and complexity of data sets continues to increase, the development of user interfaces and interaction techniques that expedite the process of exploring that data must receive new attention. Regardless of the speed of rendering, it is important to coherently organize the visual process of exploration: this information both grants insights about the data to a user and can be used by collaborators to understand the results ...
 
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Image Graphs
Image Graphs captures both the process and results of visualization, and can thus be reused and shared. Making new visualizations becomes operating on image graphs. It is also possible to optimize a visualization process by analyzing the image graphs.
 
 
Image Graphs - A Novel Approach to Visual Data Exploration
Kwan-Liu Ma
In Proceedings of IEEE Visualization 1999 Conference
October, 1999, pp. 81-88
For types of data visualization where the cost of producing images is high, and the relationship between the rendering parameters and the image produced is less than obvious, a visual representation of the exploration process can make the process more efficient and effective. Image graphs represent not only the results but also the process of data visualization. Each node in an image graph consists of an image and the corresponding visualization parameters used to produce it ...
 
A Graph-based Interface for Representing Volume Visualization Results
James Patten and Kwan-Liu Ma
In Proceedings of Graphics Interface
June, 1998, pp. 117-124
This paper discusses a graph based user interface for representing the results of the volume visualization process. As images are rendered, they are connected to other images in a graph based on their rendering parameters. The user can take advantage of the information in this graph to understand how certain rendering parameter changes a ect a dataset, making the visualization process more ecient. ...
 
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Intelligent Visual Interfaces
We develop intelligent visual interfaces which integrate machine learning into the data visualization process because many tedious visualization tasks such as segmentation, feature extraction and tracking can be effectively "learned" and performed by the computer, leaving the user free to concentrate on data understanding through an simple, intuitive user interface.
 
 
Intelligent Feature Extraction and Tracking for Visualizing Large-Scale 4D Flow Simulations
Fan-Yin Tzeng and Kwan-Liu Ma
In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC
November, 2005
Terascale simulations produce data that is vast in spatial, emporal, and variable domains, creating a formidable challenge for subsequent analysis. Feature extraction as a data reduction method offers a viable solution to this large data problem. This paper presents a new approach to the problem of extracting and visualizing 4D features within large volume data. Conventional methods requires either an analytical description of the feature of interest or tedious manual intervention throughout the feature extraction and tracking process ...
 
An Intelligent System Approach to Higher-Dimensional Classification of Volume Data
Fan-Yin Tzeng, Eric Lum, and Kwan-Liu Ma
IEEE Transactions on Visualization and Computer Graphics
Volume 11, Number 3, May/June, 2005, pp. 273-284
In volume data visualization, the classification step is used to determine voxel visibility and is usually carried out through the interactive editing of a transfer function that defines a mapping between voxel value and color/opacity. This approach is limited by the difficulties in working effectively in the transfer function space beyond two dimensions. We present a new approach to the volume classification problem which couples machine learning and a painting metaphor to allow more sophisticated classification in an intuitive manner ...
 
A Cluster-Space Visual Interface for Arbitrary Dimensional Classification of Volume Data
Fan-Yin Tzeng and Kwan-Liu Ma
In Proceedings of Joint Eurographics-IEEE TVCG Symposium on Visualization
May, 2004, pp. 17-24
In volume visualization, users typically specify transfer functions to classify the data and assign visual attributes to each material class. Higher-dimensional classication makes it easier to differentiate material classes since more data properties are considered. One of the difculties in using higher-dimensional classication is the absence of appropriate user interfaces. We introduce an intuitive user interface that allows the user to work in the cluster space, which shows the material classes with a set of material widgets, rather than work in the transfer function space ...
 
A Novel Interface for Higher-Dimensional Classification of Volume Data
Fan-Yin Tzeng, Eric Lum, and Kwan-Liu Ma
In Proceedings of IEEE Visualization 2003 Conference
October, 2003, pp. 505-512
In the traditional volume visualization paradigm, the user specifies a transfer function that assigns each scalar value to a color and opacity by defining an opacity and a color map function. The transfer function has two limitations. First, the user must define curves based on histogram and value rather than seeing and working with the volume itself. Second, the transfer function is inflexible in classifying regions of interest, where values at a voxel such as intensity and gradient are used to differentiate material, not taking into account additional properties such as texture and position ...
 
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