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Friday, October 05, 2007

GFX Cafe Seminar Friday Oct 5, 2007

GFX Café Seminar Friday October 5, 2007
12noon, ECE 118

Food will be served

Visualizing Uncertainty
by Joe Kniss, Advanced Graphics Lab

Visualization users are increasingly in need of techniques for assessing quantitative uncertainty and error in the images produced. Statistical segmentation algorithms compute these quantitative results, yet volume
rendering tools typically produce only qualitative imagery via transfer function-based classification. This paper presents a visualization technique that allows users to interactively explore the uncertainty, risk, and
probabilistic decision of surface boundaries.

Our approach makes it possible to directly visualize the combined "fuzzy" classification results from multiple segmentations by combining these data into a unified probabilistic data space. We represent this unified space, the combination of scalar volumes from numerous segmentations, using a novel graph-based dimensionality reduction scheme. The scheme both dramatically reduces the dataset size and is suitable for efficient, high quality, quantitative visualization. Lastly, we show that the statistical risk  arising from overlapping segmentations is a robust measure for visualizing features and assigning optical properties.

Joe is an Assistant Professor in Computer Science an member of the Advanced Graphics Lab at UNM.  His research focuses on interactive visualization of large datasets, light transport, and computer graphics.   His current research investigates methods for capturing and displaying uncertainty introduced by the data processing pipeline, and frameworks for representing non-linear features in band-limited raster image data.

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Advanced Graphics Lab
Dept. of Electrical & Computer Engineering
University of New Mexico
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