PRISM is a focal point of interdisciplinary research in geometric modeling, computer graphics and visualization at Arizona State University. Many projects in the last ten years have involved laser
scanning, geometric modeling and feature extraction from such data as archaeological vessels, bones, human faces, etc. This paper gives a brief overview of a recently completed project on the 3D reconstruction of George Washington (GW). The project brought together forensic anthropologists, digital artists and computer scientists in the 3D digital reconstruction of GW at 57, 45 and 19 including detailed heads and bodies. Although many other scanning projects such as the Michelangelo project have successfully captured fine details via laser scanning, our project took it a step further,
i.e. to predict what that individual (in the sculpture) might have looked like both in later and earlier years, specifically the process to account for reverse aging. Our base data was GWs face mask at Morgan Library and Hudons bust of GW at Mount Vernon, both done when GW was 53. Additionally, we scanned the statue at the Capitol in Richmond, VA; various dentures, and other items. Other measurements came from clothing and even portraits of GW. The digital GWs were
then milled in high density foam for a studio to complete the work. These will be unveiled at the opening of the new education center at Mt Vernon in fall 2006.
Line and net patterns in a noisy environment exist in many biomedical images. Examples include: Blood vessels in angiography, white matter in brain MRI scans, and cell spindle fibers in confocal microscopic data. These piecewise linear patterns with a Gaussian-like profile can be differentiated from others by their distinctive shape characteristics. A shape-based modeling method is developed to enhance and segment line and net patterns. The algorithm is implemented in an enhancement/thresholding type of edge operators. Line and net features are enhanced by second partial derivatives and segmented by thresholding. The method is tested on synthetic, angiography, MRI, and confocal microscopic data. The results are compared to the implementation of matched filters and crest lines. It shows that our new method is robust and suitable for different types of data in a broad range of noise levels.