Full Bayesian approach to curve and surface reconstruction

Daniel Keren, Michael Werman

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

When interpolating incomplete data, a parametric model can be chosen, or a more general approach can be considered and use a nonparametric model which allows a very large class of interpolants. A popular non-parametric model for interpolating various types of data is based on regularization, which looks for an interpolant that is both close to the data and also smooth in some sense. The classical approach to regularization is select optimal weights that should be assigned to these two terms, and minimize the resulting error functional. A method which uses full probability distribution on the space of admissable functions, as opposed to the probability induced by using a single combination of weights, is proposed.

Original languageAmerican English
Pages (from-to)27-43
Number of pages17
JournalJournal of Mathematical Imaging and Vision
Volume11
Issue number1
DOIs
StatePublished - 1999

Bibliographical note

Funding Information:
This research has been sponsored by the U.S. Office of Naval Research under Grant N00014-93-1-1202, R&T Project Code 4424341—01.

Fingerprint

Dive into the research topics of 'Full Bayesian approach to curve and surface reconstruction'. Together they form a unique fingerprint.

Cite this