Modeling and estimation of multiresolution stochastic processes (1992)
| Venue: | IEEE TRANS. ON INFORMATION THEORY |
| Citations: | 85 - 14 self |
BibTeX
@ARTICLE{Basseville92modelingand,
author = {Michele Basseville and Albert Benveniste and Kenneth C. Chou and Stuart A. Golden and Ramine Nikoukhah and Alan S. Willsky},
title = {Modeling and estimation of multiresolution stochastic processes},
journal = {IEEE TRANS. ON INFORMATION THEORY},
year = {1992},
volume = {38},
number = {2}
}
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Abstract
An overview is provided of the several components of a research effort aimed at the development of a theory of multiresolution stochastic modeling and associated techniques for optimal multiscale statistical signal and image processing. As described, a natural framework for developing such a theory is the study of stochastic processes indexed by nodes on lattices or trees in which different depths in the tree or lattice correspond to different spatial scales in representing a signal or image. In particular, it will be seen how the wavelet transform directly suggests such a modeling paradigm. This perspective then leads directly to the investigation of several classes of dynamic models and related notions of “ multiscale stationarity ” in which scale plays the role of a time-like variable. Focus is primarily on the investigation of models on homogenous trees. In particular, the elements of a dynamic system theory on trees are described
