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Multiscale Hidden Markov Models for Bayesian Image Analysis

  • Chapter
Bayesian Inference in Wavelet-Based Models

Part of the book series: Lecture Notes in Statistics ((LNS,volume 141))

Abstract

Bayesian multiscale image analysis weds the powerful modeling framework of probabilistic graphs with the intuitively appealing and computationally tractable multiresolution paradigm. In addition to providing a very natural and useful framework for modeling and processing images, Bayesian multiscale analysis is often much less computationally demanding compared to classical Markov random field models. This chapter focuses on a probabilistic graph model called the multiscale hidden Markov model (MHMM), which captures the key inter-scale dependencies present in natural signals and images. A common framework for the MHMM is presented that is capable of analyzing both Gaussian and Poisson processes, and applications to Bayesian image analysis are examined.

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Authors
  1. Robert D. Nowak
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Editors and Affiliations

  1. Institute of Statistics and Decision Sciences, Duke University, Box 90251, Durham, 27708-0251, NC, England

    Peter Müller  & Brani Vidakovic  & 

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Nowak, R.D. (1999). Multiscale Hidden Markov Models for Bayesian Image Analysis. In: Müller, P., Vidakovic, B. (eds) Bayesian Inference in Wavelet-Based Models. Lecture Notes in Statistics, vol 141. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0567-8_16

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  • DOI: https://doi.org/10.1007/978-1-4612-0567-8_16

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-98885-6

  • Online ISBN: 978-1-4612-0567-8

  • eBook Packages: Springer Book Archive

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