A Next Step: Visualizing Errors and Uncertainty

Johnson, Chris R.; Sanderson, Allen R.

doi:10.1109/mcg.2003.1231171

Cited by 239 publications

(138 citation statements)

References 9 publications

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“…Such a nested surfaces approach (used by, e.g., Tory et al [16]) shows all context information. Similar approaches have been proposed in uncertainty visualization (see, e.g., Johnson and Sanderson [17]). However, in these visualizations the identification of differences is left to the user.…”

Section: Comparative Visualization Of 3d Surfacesmentioning

confidence: 94%

Image-based rendering of intersecting surfaces for dynamic comparative visualization

et al. 2010

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Nested or intersecting surfaces are proven techniques for visualizing shape differences between static 3D objects (Weigle and Taylor II, IEEE Visualization, Proceedings, pp. 503-510, 2005). In this paper we present an imagebased formulation for these techniques that extends their use to dynamic scenarios, in which surfaces can be manipulated or even deformed interactively. The formulation is based on our new layered rendering pipeline, a generic image-based approach for rendering nested surfaces based on depth peeling and deferred shading.We use layered rendering to enhance the intersecting surfaces visualization. In addition to enabling interactive performance, our enhancements address several limitations of the original technique. Contours remove ambiguity regard- ing the shape of intersections. Local distances between the surfaces can be visualized at any point using either depth fogging or distance fields: Depth fogging is used as a cue for the distance between two surfaces in the viewing direction, whereas closest-point distance measures are visualized interactively by evaluating one surface's distance field on the other surface. Furthermore, we use these measures to define a three-way surface segmentation, which visualizes regions of growth, shrinkage, and no change of a test surface compared with a reference surface.Finally, we demonstrate an application of our technique in the visualization of statistical shape models. We evaluate our technique based on feedback provided by medical image analysis researchers, who are experts in working with such models.

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Section: Comparative Visualization Of 3d Surfacesmentioning

confidence: 94%

Image-based rendering of intersecting surfaces for dynamic comparative visualization

et al. 2010

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“…For example, Johnson and Sanderson [8] discussed the need for uncertainty visualization in the scientific imaging field. Typically, error and uncertainty of data are included as 2D graphs but left out of two-dimensional and three-dimensional visualizations.…”

Section: Related Workmentioning

confidence: 99%

Visualization of Covariance and Cross-Covariance Fields

Yang

Xiu

Kirby

2013

Int. J. UncertaintyQuantification

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“…In addition, visualization researchers have developed few methods that allow for easy comparison and representation of error and uncertainty in data for visualizations. To make current visualization techniques and software more useful to biomedical computing (and other) researchers, we need to incorporate visual representations of error and uncertainty [119]. A few visualization researchers have started thinking about how to create effective three-dimensional visual representations of errors and uncertainties, the sources of which can include uncertainty in acquisition (instrument measurement error, numerical analysis error, statistical variation), uncertainty in the model (both in mathematical and in geometric models), uncertainty in transformation (where errors may be introduced from resampling, filtering, quantization, rescaling), and uncertainty in visualization.…”

Section: Error and Uncertainty Visualizationmentioning

confidence: 99%