Using Importance Sampling for Bayesian Feature Space Filtering

  • Anders Brun
  • Björn Svensson
  • Carl-Fredrik Westin
  • Magnus Herberthson
  • Andreas Wrangsjö
  • Hans Knutsson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

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.

Keywords

Importance Sampling Scalar Image Computational Framework Rician Noise Trial Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Anders Brun
    • 1
    • 4
  • Björn Svensson
    • 1
    • 4
  • Carl-Fredrik Westin
    • 2
  • Magnus Herberthson
    • 3
  • Andreas Wrangsjö
    • 1
    • 4
  • Hans Knutsson
    • 1
    • 4
  1. 1.Department of Biomedical Engineering, Linköping UniversitySweden
  2. 2.Department of Mathematics, Linköping UniversitySweden
  3. 3.Laboratory of Mathematics in Imaging, Harvard Medical School, BostonUSA
  4. 4.Center for Medical Image Science and Visualization, Linköping UniversitySweden

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