Abstract:

We propose a new approach to the problem of generating a simple
topologically-closed geometric model from a point-sampled volume
data set. We call such a model a Geometrically Deformed Model
or GDM. A GDM is created by placing a 'seed' model in the volume
data set. The model is then deformed by a relaxation process
that minimizes a set of constraints that provides a measure of
how well the model fits the features in the data. Constraints are
associated with each vertex in the model that control local
deformation, interaction between the model and the data set, and
the shape and topology of the model. Once generated, a GDM can be
used for visualization, shape recognition, geometric measurements,
or subjected to a series of geometric operations. This technique
is of special importance because of the advent of nondestructive
sensing equipment (CT, MRI) that generates point samples of true
three-dimensional objects.

Last modified on September 8, 2003.