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Hierarchical Multiscale Modeling of Wavelet-Based Correlations

  • Zohreh Azimifar
  • Paul Fieguth
  • Ed Jernigan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)

Abstract

This paper presents a multiscale-based analysis of the statistical dependencies between the wavelet coefficients of random fields. In particular, in contrast to common decorrelated-coefficient models, we find that the correlation between wavelet scales can be surprisingly substantial, even across several scales. In this paper we investigate eight possible choices of statistical-interaction models, from trivial models to wavelet-based hierarchical Markov stochastic processes. Finally, the importance of our statistical approach is examined in the context of Bayesian estimation.

Keywords

Wavelet Transform Wavelet Domain Wavelet Basis Function Hide Markov Tree Hide Markov Tree Model 
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-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Zohreh Azimifar
    • 1
  • Paul Fieguth
    • 1
  • Ed Jernigan
    • 1
  1. 1.Department of Systems Design EngineeringUniversity of WaterlooWaterlooCanada

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