Probabilistic surfaces point based primitives to show surface uncertainty

• Published in: 13th IEEE Visualization 2002 Conference (VIS 2002) pp. 147 - 154
• Main Authors: Grigoryan, Gevorg, Rheingans, Penny
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 27.10.2002; IEEE
• Series: ACM Conferences
• Subjects: Applied computing > Document management and text processing > Document capture > Graphics recognition and interpretation; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Computing methodologies > Artificial intelligence > Computer vision > Image and video acquisition > 3D imaging; Computing methodologies > Computer graphics; Computing methodologies > Computer graphics > Animation; Exact sciences and technology; Pattern recognition. Digital image processing. Computational geometry; uncertainty; visualizing surface uncertainty; points as display primitives; Growing phase; Computational geometry; Visualization; Uncertainty; Medical imagery; Tumor; Visual information; Polygon; Display
• ISBN: 9780780374980; 0780374983
• DOI: 10.5555/602099.602121

Efficient and informative visualization of surfaces with uncertainties is an important topic with many applications in science and engineering. Examples include environmental pollution borderline identification, identification of the limits of an oil basin, or discrimination between contaminated and healthy tissue in medicine. This paper presents an approach for such visualization using points as display primitives. Our approach is to render each polygon as a collection of points and to displace each point from the surface in the direction of the surface normal by an amount proportional to some random number multiplied by the uncertainty level at that point. This approach can be used in combination with other techniques such as pseudo-coloring and shading to give rise to efficient and revealing visualizations. The method is used to visualize real and simulated tumor formations with uncertainty of tumor boundaries.