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.
The support of the Natural Science & Engineering Research Council of Canada and Communications & Information Technology Ontario are acknowledged.
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© 2002 Springer-Verlag Berlin Heidelberg
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Azimifar, Z., Fieguth, P., Jernigan, E. (2002). Hierarchical Multiscale Modeling of Wavelet-Based Correlations. In: Caelli, T., Amin, A., Duin, R.P.W., de Ridder, D., Kamel, M. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2002. Lecture Notes in Computer Science, vol 2396. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-70659-3_90
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DOI: https://doi.org/10.1007/3-540-70659-3_90
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