Task-Driven Color Coding

Tominski, Christian; Fuchs, Georg; Schumann, Heidrun

doi:10.1109/iv.2008.24

Cited by 75 publications

(73 citation statements)

References 9 publications

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“…Based on fundamental human perceptual principles and the type of data being displayed (sequential, diverging, or categorical), there are formal and systematic ways to make an appropriate color choice based on the task at hand [14,27,33,38,46]. Based on laboratory user studies, specific guidelines are available for the effective design of color maps [32,36,49].…”

Section: Color Map Evaluationsmentioning

confidence: 99%

Evaluation of Artery Visualizations for Heart Disease Diagnosis

Borkin

Gajos

Peters

et al. 2011

IEEE Trans. Visual. Comput. Graphics

142

117

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Abstract-Heart disease is the number one killer in the United States, and finding indicators of the disease at an early stage is critical for treatment and prevention. In this paper we evaluate visualization techniques that enable the diagnosis of coronary artery disease. A key physical quantity of medical interest is endothelial shear stress (ESS). Low ESS has been associated with sites of lesion formation and rapid progression of disease in the coronary arteries. Having effective visualizations of a patient's ESS data is vital for the quick and thorough non-invasive evaluation by a cardiologist. We present a task taxonomy for hemodynamics based on a formative user study with domain experts. Based on the results of this study we developed HemoVis, an interactive visualization application for heart disease diagnosis that uses a novel 2D tree diagram representation of coronary artery trees. We present the results of a formal quantitative user study with domain experts that evaluates the effect of 2D versus 3D artery representations and of color maps on identifying regions of low ESS. We show statistically significant results demonstrating that our 2D visualizations are more accurate and efficient than 3D representations, and that a perceptually appropriate color map leads to fewer diagnostic mistakes than a rainbow color map.

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Section: Color Map Evaluationsmentioning

confidence: 99%

Evaluation of Artery Visualizations for Heart Disease Diagnosis

Borkin

Gajos

Peters

et al. 2011

IEEE Trans. Visual. Comput. Graphics

142

117

View full text Add to dashboard Cite

show abstract

“…Data values can be mapped on different visual variables, such as color (a, d), size (b, e), or direction (c, f). Color scales can be freely adjusted, depending on the task at hand [46]. For our design, we used standard color scales from Color Brewer [47].…”

Section: Data Visualizationmentioning

confidence: 99%

Visualizing 3D Terrain, Geo-Spatial Data, and Uncertainty

et al. 2017

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Visualizing geo-spatial data embedded into a three-dimensional terrain is challenging. The problem becomes even more complex when uncertainty information needs to be presented as well. This paper addresses the question of how to visually communicate all three aspects: the 3D terrain, the geo-spatial data, and the data-associated uncertainty. We argue that visualizing all aspects with a high degree of detail will likely exceed the visual budget. Therefore, we propose a visualization strategy based on prioritizing a selected aspect and presenting the remaining two with less detail. We discuss various design options that allow us to obtain differently prioritized visual representations. Our approach has been implemented as a tool for rapid visualization prototyping in the context of avionics applications. Practical solutions are described for a use case related to the visualization of 3D terrain and uncertain weather data.

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“…Then the visual representation changes to a simple color-coded table that visualizes multiple variables for all 12 months for the area above which the tangible view is hovering (see Figure 1(c)). Depending on whether the user's task is to identify data values or to locate data values, different color schemes are used to encode data values (see Figure 5) [37]. Simply flipping the tangible view switches between both tasks.…”

Section: Case Study: Space-time-cube Visualizationmentioning

confidence: 99%

Tangible views for information visualization

Spindler

Tominski

Schumann

et al. 2010

ACM International Conference on Interactive Tabletops and Surfaces

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In information visualization, interaction is commonly carried out by using traditional input devices, and visual feedback is usually given on desktop displays. By contrast, recent advances in interactive surface technology suggest combining interaction and display functionality in a single device for a more direct interaction. With our work, we contribute to the seamless integration of interaction and display devices and introduce new ways of visualizing and directly interacting with information. Rather than restricting the interaction to the display surface alone, we explicitly use the physical three-dimensional space above it for natural interaction with multiple displays. For this purpose, we introduce tangible views as spatially aware lightweight displays that can be interacted with by moving them through the physical space on or above a tabletop display's surface. Tracking the 3D movement of tangible views allows us to control various parameters of a visualization with more degrees of freedom. Tangible views also facilitate making multiple -previously virtual -views physically "graspable". In this paper, we introduce a number of interaction and visualization patterns for tangible views that constitute the vocabulary for performing a variety of common visualization tasks. Several implemented case studies demonstrate the usefulness of tangible views for widely used information visualization approaches and suggest the high potential of this novel approach to support interaction with complex visualizations.

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Task-Driven Color Coding

Cited by 75 publications

References 9 publications

Evaluation of Artery Visualizations for Heart Disease Diagnosis

Evaluation of Artery Visualizations for Heart Disease Diagnosis

Visualizing 3D Terrain, Geo-Spatial Data, and Uncertainty

Tangible views for information visualization

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