Bayesian Surface Reconstruction from Noisy Images

Reconstruction of surfaces from images alone is usually difficult due
to noise. Prior information such as smoothness, shape, size etc. may
however be available. The Bayesian framework makes it possible for a
formal integration of such prior information and the observed data.
Furthermore, the development of the Markov Chain Monte Carlo method
makes it possible for simulation from the posterior of the surface
given the observed images.

In Storvik (1994) this approach was applied to reconstruction of
contours of objects in two-dimensional images. Similar approaches has
been used in Mardia and Qian among others. Extending these approaches
to three dimensions and/or time is in principle simple, but both the
cost of implementation and the computer power needed for performing
simulations are high.

In this talk we will discuss the use of Bayesian modeling and
stochastic sampling for reconstruction of surfaces both in two and
three dimensions. Construction of appropriate models, algorithms and
estimation procedures for the parameters involved will be considered.
Examples from medical imaging will be presented.