Building perceptual textures to visualize multidimensional datasets

Healey,; Enns,

doi:10.1109/visual.1998.745292

Cited by 27 publications

(37 citation statements)

References 20 publications

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“…Kuhn et al used results from color perception experiments to recolor images in ways that allow colorblind viewers to properly perceive color differences [120]. Other visual features have been studied in a similar fashion, producing guidelines on the use of texture-size, orientation, and regularity [121], [122], [123]-and motion-icker, direction, and velocity [38], [124]-for visualizing data.…”

Section: Visual Attentionmentioning

confidence: 99%

Attention and Visual Memory in Visualization and Computer Graphics

Healey

Enns

2012

IEEE Trans. Visual. Comput. Graphics

308

179

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Abstract-A fundamental goal of visualization is to produce images of data that support visual analysis, exploration, and discovery of novel insights. An important consideration during visualization design is the role of human visual perception. How we "see" details in an image can directly impact a viewer's ef ciency and effectiveness. This article surveys research on attention and visual perception, with a speci c focus on results that have direct relevance to visualization and visual analytics. We discuss theories of low-level visual perception, then show how these ndings form a foundation for more recent work on visual memory and visual attention. We conclude with a brief overview of how knowledge of visual attention and visual memory is being applied in visualization and graphics. We also discuss how challenges in visualization are motivating research in psychophysics.

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Section: Visual Attentionmentioning

confidence: 99%

Attention and Visual Memory in Visualization and Computer Graphics

Healey

Enns

2012

IEEE Trans. Visual. Comput. Graphics

308

179

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“…Previous research in our laboratory has shown that the low-level human visual system processes these features very rapidly and accurately. We have conducted numerous controlled experiments to define the information-carrying capacity of each feature, both in isolation, and in combination with other features [3], [36], [37], [38]. The result is a multidimensional visualization system that helps viewers to locate and examine data elements within the data set that are likely to be of interest.…”

Section: Navigation Frameworkmentioning

confidence: 99%

Interest Driven Navigation in Visualization

Healey

Dennis

2012

IEEE Trans. Visual. Comput. Graphics

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Abstract-This paper describes a new method to explore and discover within a large data set. We apply techniques from preference elicitation to automatically identify data elements that are of potential interest to the viewer. These "elements of interest (EOI)" are bundled into spatially local clusters, and connected together to form a graph. The graph is used to build camera paths that allow viewers to "tour" areas of interest (AOI) within their data. It is also visualized to provide wayfinding cues. Our preference model uses Bayesian classification to tag elements in a data set as interesting or not interesting to the viewer. The model responds in real time, updating the elements of interest based on a viewer's actions. This allows us to track a viewer's interests as they change during exploration and analysis. Viewers can also interact directly with interest rules the preference model defines. We demonstrate our theoretical results by visualizing historical climatology data collected at locations throughout the world.

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“…Our most recent research has focused on the combined use of the fundamental properties of color (hue and luminance) and texture (size, contrast, orientation, and regularity) to encode multiple attributes in a single display [Healey 1996;Healey and Enns 1998;1999]. A comparison of perceptual color and texture properties with painting styles from Impressionist art reveals a strong correspondence between the two.…”

Section: Perceptual Propertiesmentioning

confidence: 99%

Perceptually based brush strokes for nonphotorealistic visualization

et al. 2004

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An important problem in the area of computer graphics is the visualization of large, complex information spaces. Datasets of this type have grown rapidly in recent years, both in number and in size. Images of the data stored in these collections must support rapid and accurate exploration and analysis. This article presents a method for constructing visualizations that are both effective and aesthetic. Our approach uses techniques from master paintings and human perception to visualize a multidimensional dataset. Individual data elements are drawn with one or more brush strokes that vary their appearance to represent the element's attribute values. The result is a nonphotorealistic visualization of information stored in the dataset. Our research extends existing glyph-based and nonphotorealistic techniques by applying perceptual guidelines to build an effective representation of the underlying data. The nonphotorealistic properties the strokes employ are selected from studies of the history and theory of Impressionist art. We show that these properties are similar to visual features that are detected by the lowlevel human visual system. This correspondence allows us to manage the strokes to produce perceptually salient visualizations. Psychophysical experiments confirm a strong relationship between the expressive power of our nonphotorealistic properties and previous findings on the use of perceptual color and texture patterns for data display. Results from these studies are used to produce effective nonphotorealistic visualizations. We conclude by applying our techniques to a large, multidimensional weather dataset to demonstrate their viability in a practical, real-world setting.

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Building perceptual textures to visualize multidimensional datasets

Cited by 27 publications

References 20 publications

Attention and Visual Memory in Visualization and Computer Graphics

Attention and Visual Memory in Visualization and Computer Graphics

Interest Driven Navigation in Visualization

Perceptually based brush strokes for nonphotorealistic visualization

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