Using Importance Sampling for Bayesian Feature Space Filtering [chapter]

Anders Brun, Björn Svensson, Carl-Fredrik Westin, Magnus Herberthson, Andreas Wrangsjö, Hans Knutsson
Image Analysis  
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
more » ... ty functions. In this novel computational framework we are able to efficiently filter both vector-valued images and data, similar to e.g. the wellknown bilateral, median and mean shift filters.
doi:10.1007/978-3-540-73040-8_83 dblp:conf/scia/BrunSWHWK07 fatcat:c4szp4k7oneibozc35lr67lkpy