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Textures and Wavelet-Domain Joint Statistics

  • Zohreh Azimifar
  • Paul Fieguth
  • Ed Jernigan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3212)

Abstract

This paper presents an empirical study of the joint wavelet statistics for textures and other random imagery. There is a growing realization that modeling wavelet coefficients as independent, or at best correlated only across scales, assuming independence within a scale, may be a poor assumption. While recent developments in wavelet-domain Hidden Markov Models (notably HMT-3S) account for within-scale dependencies, we find empirically that wavelet coefficients exhibit within- and across-subband neighborhood activities which are orientation dependent. Surprisingly these structures are not considered by the state-of-the-art wavelet modeling techniques. In this paper we describe possible choices of the wavelet statistical interactions by examining the joint-histograms, correlation coefficients, and the significance of coefficient relationships.

Keywords

Hide Markov Model Hide State Wavelet Domain Wavelet Statistic Joint Histogram 
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 2004

Authors and Affiliations

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

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