PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Distributed high dimensional information theoretical image registration via random projections
Zoltan Szabo and András Lorincz
Digital Signal Processing 2012.

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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.

EPrint Type:Article
Additional Information:In press version: http://dx.doi.org/10.1016 j.dsp.2012.04.018
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Theory & Algorithms
ID Code:9552
Deposited By:Zoltan Szabo
Deposited On:11 July 2012

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