ViA: a perceptual visualization assistant

Chris G. Healey; Robert St. Amant; Mahmoud S. Elhaddad

doi:10.1117/12.384859

5 May 2000 ViA: a perceptual visualization assistant

Chris G. Healey, Robert St. Amant, Mahmoud S. Elhaddad

Proceedings Volume 3905, 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making; (2000) https://doi.org/10.1117/12.384859
Event: 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, 1999, Washington, DC, United States

Abstract

This paper describes an automated visualized assistant called ViA. ViA is designed to help users construct perceptually optical visualizations to represent, explore, and analyze large, complex, multidimensional datasets. We have approached this problem by studying what is known about the control of human visual attention. By harnessing the low-level human visual system, we can support our dual goals of rapid and accurate visualization. Perceptual guidelines that we have built using psychophysical experiments form the basis for ViA. ViA uses modified mixed-initiative planning algorithms from artificial intelligence to search of perceptually optical data attribute to visual feature mappings. Our perceptual guidelines are integrated into evaluation engines that provide evaluation weights for a given data-feature mapping, and hints on how that mapping might be improved. ViA begins by asking users a set of simple questions about their dataset and the analysis tasks they want to perform. Answers to these questions are used in combination with the evaluation engines to identify and intelligently pursue promising data-feature mappings. The result is an automatically-generated set of mappings that are perceptually salient, but that also respect the context of the dataset and users' preferences about how they want to visualize their data.

Citation Download Citation

Chris G. Healey, Robert St. Amant, and Mahmoud S. Elhaddad "ViA: a perceptual visualization assistant", Proc. SPIE 3905, 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, (5 May 2000); https://doi.org/10.1117/12.384859

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