G. Dudek, F. Ferrie, N. Ayoung-Chee A key problem in vision is the identification of objects from their shape. In part, what makes the computational definition of shape so difficult is the need to extract key structures robustly despite a host of confounding processes. This project deals with the description and recognition of shapes at both a local and global level. The approach is based on the use of global parametric functions as well as curvature information to yield a set of shape descriptors at different spatial scales over both curves and surfaces. Two approaches are being integrated, one is based on regularization-like minimization with a family of different ``stabilizing functionals'' each with an a priori preference for a different type of structure, the other is a more conventional interpolation-based scheme. The objectives of this research are to: describe objects based on both local and non-local properties, to define algorithms for matching and hence recognizing objects described by three-dimensional surface data, and to elaborate techniques for combining multiple sources of information within this representation framework.