Publications

Reports

Engaging Viewers Through Nonphotorealistic Visualizations [PDF, 9227K] [Presentation, 8/04/07, 45.5MB][Fast Forward presentation, 8/07/07, 7.4MB]

Research in human visual cognition suggests that beautiful images can engage the visual system, encouraging it to linger in certain locations in an image and absorb subtle details. By developing aesthetically pleasing visualizations of data, we aim to engage viewers and promote prolonged inspection, which can lead to new discoveries within the data. We present three new visualization techniques that apply painterly rendering styles to vary interpretational complexity (IC), indication and detail (ID), and visual complexity (VC), image properties that are important to aesthetics. Knowledge of human visual perception and psychophysical models of aesthetics provide the theoretical basis for our designs. Computational geometry and nonphotorealistic algorithms are used to preprocess the data and render the visualizations. We demonstrate the techniques with visualizations of real weather and supernova data.

Tateosian, L. G., Healey, C. G., and Enns, J. T. "Engaging Viewers Through Nonphotorealistic Visualizations." In Proceedings of the 5th International Symposium on Non-Photorealistic Animation and Rendering (San Diego, California, August 04 - 05, 2007). NPAR '07. ACM, New York, NY, 93-102.

Investigating Aesthetic Visualizations [PDF, 12.8MB] [Presentation, 12/15/06, 38.4MB]

Visualizations enable scientists to inspect, interpret, and analyze large multi-dimensional data sets. Effective visualizations are designed to both orient and engage viewers by directing attention in response to a visual stimulus, and then encouraging a viewer's vision to linger at a given image location. Research into human visual perception provides information about how to orient viewers, using salient visual features, such as color, orientation, and flicker. Less is known about how to build engaging visualizations. Increasing the aesthetic merit of visualizations is a promising approach to increasing engagement. Intuition suggests that visualizations with a more aesthetic presentation style will be judged as more artistic, but this is an open problem. In this thesis, we explored an important question pertaining to creating aesthetic visualizations: Is it possible to affect the perceived artistic merit of a scientific visualization?

To investigate this question, we developed three new painterly visualization techniques, designed to vary different visual qualities important to aesthetics: interpretational complexity (IC), indication and detail (ID), and visual complexity (VC). We conducted four experiments to investigate how these qualities affect the aesthetics. Observers were asked to rank IC, ID, and VC images, together with Master abstract and Impressionist paintings on five questions: artistic merit, pleasure, arousal, meaningfulness, and complexity. Although realistic Impressionist paintings consistently ranked as most artistic, computer visualizations were considered as artistic as and more pleasing than Master abstractionist artwork in certain situations. There was also a significant preference for aesthetic visualizations that used more sophisticated presentation styles. This provides strong evidence that our aesthetic techniques can increase the perceived artistic merit of a visualization, possibly leading to a significant improvement in the visualizations's ability to engage its viewers.

We applied our experimental techniques to real meteorological and supernova data sets, to explore their capabilities in a real-world setting. Anecdotal feedback from a domain expert in astrophysics was strongly positive, further supporting the theory that enhancing the artistic merit of visualizations is a worthwhile contribution to the scientific community.

Tateosian, L. G., "Investigating Aesthetic Visualizations." Ph.D. Dissertation (2006) Department of Computer Science, North Carolina State University.

Stevens Dot Patterns for 2D Flow Visualization [PDF, 1523K]

This paper describes a new technique to visualize 2D flow fields with a sparse collection of dots. A cognitive model proposed by Ken Stevens describes how spatially local configurations of dots are processed in parallel by the low-level visual system to perceive orientations throughout the image. We integrate this model into a visualization algorithm that converts a sparse grid of dots into patterns that capture flow orientations in an underlying flow field. Because our visualizations are based on experimental results from human vision, the patterns are perceptually salient. We describe how our algorithm supports large flow fields that exceed the capabilities of a display device, and demonstrate how to include properties like direction and velocity in our visualizations. We conclude by applying our technique to 2D slices from a simulated supernova collapse.

Tateosian, L. G., Dennis, B. M., and Healey, C. G. "Stevens Dot Patterns for 2D Flow Visualization." In Third International Symposium on Applied Perception in Graphics and Visualization (Boston, Massachusetts, 2006), pp.93-100.

Designing a Visualization Framework for Multidimensional Data [PDF, 1158K]

This article describes our initial end-to-end system that starts with data management and continues through assisted visualization design, display, navigation, and user interaction. The purposes of this discussion are to: (1) promote a more comprehensive visualization framework; (2) describe how expertise from human psychophysics, databases, rational logic, and artificial intelligence can be applied to visualization; and (3) illustrate the benefits of a more complete framework using examples from our own experiences.

Dennis, B. M., Kocherlakota, S. M., Sawant, A. P., Tateosian, L. G., and Healey, C. G. "Designing a Visualization Framework for Multidimensional Data." IEEE Computer Graphics & Applications (Visualization Viewpoints) 25, 6, (2005), 10-15.

Perceptually-Based Brush Strokes for Nonphotorealistic Visualization [PDF, 4243K]

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 paper 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 low-level 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.

Healey, C. G., Enns, J. T., Tateosian, L. G., and Remple, M. "Perceptually-Based Brush Strokes for Nonphotorealistic Visualization." ACM Transactions on Graphics 23, 1, (2004), 64-96.

Nonphotorealistic Visualization of Multidimensional Datasets [PDF, 1210K] [Presentation, 12/19/02, 7573K]

The huge quantities of data that are being recorded annually need to be organized and analyzed. The datasets often consist of a large number of elements, each associated with multiple attributes. Our objective is to create effective, aesthetically appealing multidimensional visualizations. By mapping element attributes to carefully chosen visual features, such visualizations support exploration, encourage prolonged inspection, and facilitate discovery of unexpected data characteristics and relationships.

We present a new visualization technique that uses “painted” brush strokes to represent data elements of large multidimensional datasets. Each element’s attributes controls the visual features of one or more brushstrokes. To pursue aesthetic appeal, we draw inspiration from the Impressionist style of painting and apply rendering techniques from nonphotorealistic graphics. We construct our mappings to harness the strengths of the human visual system. The resulting displays are nonphotorealistic visualizations of the information in the datasets.

Studies confirm that existing guidelines based on human visual perception apply to our painterly styles. Additional studies investigate the artistic appeal of our visualizations, along with the emotional and visual features that influence aesthetic judgments. Finally, we use the results of these studies to combine painterly styles to build a tool which creates visualizations that are both effective and aesthetic and we apply our method to a real-world dataset.

Tateosian, L. G., "Nonphotorealistic Visualization of Multidimensional Datasets." Master's Thesis (2002) Department of Computer Science, North Carolina State University.

Characterizing Aesthetic Visualizations [PDF, 2225K] [Presentation, 4/26/05, 21MB]

Exploiting aesthetics to improve the effectiveness of visualizations has not yet been explored in depth by the visualization community. This report describes a proposal to vary visual qualities derived from models of aesthetics to investigate the affect on visualizations. Visualization scientists would like to engage viewers to encourage exploration. A promising approach to engage viewers is to enhance the aesthetic appeal of the visualization. Psychologists believe that aesthetic judgement can be characterized by a number of emotional and cognitive properties. This project aims to identify some qualities that can be varied in visualizations to influence aesthetic judgment. The properties identified by psychologists provide a good starting point. In this proposal, I present three visual qualities, related to these properties. I propose to conduct studies in which these three qualities are varied, to analyze results statistically, and then to seek ways to vary these qualities in a visualization while maintaining perceptual salience.

Tateosian, L. G. "Inv." NCSU Dissertation Proposal (NCSU, ).

NPR: Art Enhancing Computer Graphics [PDF, 2164K][Presentation, 12/19/03, 19MB]

Nonphotorealistic rendering is a field in computer science in which scientists apply artistic techniques to enhance computer graphics. This paper addresses the interrogatives what, how, and why, about NPR. The discussion expands on what NPR is and what kinds of projects are being done in NPR, specifically it focuses on three issues: two large problems in NPR, simulating pen-and-ink illustration and simulating painting, and last the application of NPR to visualization. Exploring these topics thoroughly provides some specific answers to how these effects are accomplished. Throughout the paper various motivations for using NPR are revealed, including the application of NPR to visualization (as evidence of why). Our lab is interested in applying NPR techniques to visualization, so the paper concludes with some conjecture on how to verify the efficacy of this goal.

Tateosian, L. G. and Healey, C. G. "NPR: Art Enhancing Computer Graphics." Technical Report TR-2004-17 (2004), Department of Computer Science, North Carolina State University.

Last Modified by Laura G. Tateosian (1/9/08)