Stuart Andrews

Bayesian Pot-Assembly from Fragments as Problems in Perceptual-Grouping and Geometric-Learning

A heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavation site. An approach is presented to the automatic estimation of mathematical models of such pots from 3D measurements of sherds. A Bayesian approach is formulated beginning with a description of the complete set of geometric parameters that determine the distribution of the sherd measurement data. Matching of fragments and aligning them geometrically into configurations is based on matching break-curves (curves on a pot surface separating fragments), estimated axis and profile curve pairs for individual fragments and configurations of fragments, and a number of features of groups of break-curves. Pot assembly is a bottom-up maximum likelihood performance-based search. Experiments are illustrated on pots which were broken for the purpose, and on sherds from an archaeological dig located in Petra, Jordan. The performance measure can also be an aposteriori probability, and many other types of information can be included, e.g., pot wall thickness, surface color, patterns on the surface, etc. This can also be viewed as the problem of learning a geometric object from an unorganized set of free-form fragments of the object and of clutter, or as a problem of perceptual grouping.

JointViewer - An Interactive System for Exploring Orthopedic Data

We present JointViewer, a software tool to aid orthopedics researchers in exploring complex, in-vivo joint kinematics. Given bone-geometry data and bone-motion information, JointViewer models and visualizes bone inter-spacing in the joint. Next, it proposes and displays plausible ligament paths which connect bones together. Both types of models are constructed through a distancefield approach. Users can maneuver the bones in a joint for better viewing, see motion relative to a specific bone, or remove bones from a joint. We demonstrate JointViewer’s effectiveness in three applications: examining normal human wrist kinematics, capturing the effect of injury on forearm kinematics, and exploring the kinematic constraints imposed by ligaments in a pigeon shoulder. In all applications, the system effectively highlights subtle yet important relationships among bones and soft-tissue that in previous standard joint visualizations had gone unnoticed.