Distributed high dimensional information theoretical image registration via random projections

Zoltán Szabó, András Lörincz

Research output: Article

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)894-902
Number of pages9
JournalDigital Signal Processing: A Review Journal
Volume22
Issue number6
DOIs
Publication statusPublished - dec. 2012

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Applied Mathematics

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