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Perceptual Visualization Architecture

This project involves a fundamental investigation of the strengths and limitations of the low-level human visual system. We seek to identify image properties that we can see quickly, to test how these properties interact with one another, and to determine how to harness these properties. Understanding how we perceive the world around us will benefit many research areas including information display, image generation, image analysis, and the simulation of vision. One application of this knowledge is an important area of computer graphics: the visualization of large, complex, multidimensional datasets.

Visualization is the conversion of collections of strings and numbers into pictures that a viewer can use to "see" values, relationships, and structure inherent in their datasets. Multidimensional data visualization presents the dual problems of size and dimensionality. The number of sample points within the dataset is very large; moreover, each sample point contains multiple independent readings or attributes. Viewers want to visualize some or all of this information simultaneously in a single display. The goal of this proposal is to design visualizations that support rapid and accurate exploration and analysis. To do this, we must display all of the data without overwhelming the viewer's visual system.

A solution to this problem is the construction of a perceptual visualization architecture. First, a psychophysical experiments will be used to test how the low-level visual system perceives three fundamental visual features during high-speed display: color, texture, and motion. Results from these studies will form a collection of perceptual guidelines that describe how to combine color, texture, and motion patterns to represent information in an underlying dataset. A "visualization assistant" based on artificial intelligence search algorithms will be constructed on top of these guidelines. This assistant will help viewers choose perceptually optimal methods of converting their data into effective visualizations. Data will be displayed in ways that harness the strengths and avoid the limitations of the viewer's visual system. The result is a set of images that allow viewers to perform rapid, accurate, and effective exploration and analysis.


Christopher G. Healey (PI)
James T. Enns (PI, University of British Columbia)

Dan Huber (MS candidate)
Geniva Liu (PhD candidate, University of British Columbia)


Last updated Saturday, May 24, 2003