Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed High Dimensional Information Theoretical Image Registration
  via Random Projections   Information theoretical measures, such as entropy, mutual information, and
various divergences, exhibit robust characteristics in image registration
applications. However, the estimation of these quantities is computationally
intensive in high dimensions. On the other hand, consistent estimation from
pairwise distances of the sample points is possible, which suits random
projection (RP) based low dimensional embeddings. We adapt the RP technique to
this task by means of a simple ensemble method. To the best of our knowledge,
this is the first distributed, RP based information theoretical image
registration approach. The efficiency of the method is demonstrated through
numerical examples.

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The categories of this paper are: eess
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The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
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The categories of this paper are: funct-an
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The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
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The categories of this paper are: physics