Using Importance Sampling for Bayesian Feature Space Filtering
2007 (English)In: Proceedings of the 15th Scandinavian conference on image analysis / [ed] Kjær Bjarne Ersbøll and Kim Steenstrup Pedersen, Berlin, Heidelberg: Springer-Verlag , 2007, 818-827 p.Conference paper (Refereed)
We present a one-pass framework for filtering vector-valued images and unordered sets of data points in an N-dimensional feature space. It is based on a local Bayesian framework, previously developed for scalar images, where estimates are computed using expectation values and histograms. In this paper we extended this framework to handle N-dimensional data. To avoid the curse of dimensionality, it uses importance sampling instead of histograms to represent probability density functions. In this novel computational framework we are able to efficiently filter both vector-valued images and data, similar to e.g. the well-known bilateral, median and mean shift filters.
Place, publisher, year, edition, pages
Berlin, Heidelberg: Springer-Verlag , 2007. 818-827 p.
, Lecture Notes in Computer Science, ISSN 0302-9743 ; Vol. 4522
Medical and Health Sciences
IdentifiersURN: urn:nbn:se:liu:diva-38745DOI: 10.1007/978-3-540-73040-8_83ISI: 000247364000083Local ID: 45475ISBN: 978-3-540-73039-2OAI: oai:DiVA.org:liu-38745DiVA: diva2:259594
The 15th Scandinavian conference on image analysis, June 10-24, Aalborg, Denmark