ON TWO-DIMENSIONAL MARKOV SPECTRAL ESTIMATION.

R. Chellappa, Sun-Yuan Kung

Research output: Contribution to conferencePaper

1 Scopus citations

Abstract

We developed methods for two-dimensional (2-D) spectral estimation from raw data using Markov random field (MRF) models. We assume that the given finite data are represented by an appropriate Gaussian MRF model. This assumption reduces the spectral estimation problem to that of estimating the appropriate structure and the parameters of the MRF model. Parameter estimation is computationally tractable when specific boundary conditions are assumed. The toroidal lattice representation used in this paper yields computationally manageable algorithms for maximum likelihood (ML) estimation of parameters. By using the fact that the 2-D maximum entropy spectral (MES) estimate has a structure similar to the MRF power spectrum and a sample correlation matching property we argue that for infinite number of observations the MRF spectrum derived is the conventionally understood 2-D MES estimate.

Original languageEnglish (US)
Pages342-348
Number of pages7
StatePublished - Dec 1 1982
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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