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Texture analysis with variational hidden Markov trees

Publication ,  Journal Article
Dasgupta, N; Carin, L
Published in: IEEE Transactions on Signal Processing
June 1, 2006

A variational Bayes formulation of the hidden Markov tree (HMT) model is proposed for texture analysis, utilizing a multilevel wavelet decomposition of imagery. The variational method yields an approximation to the full posterior of the HMT parameters. Texture classification is based on the posterior predictive distribution or marginalized evidence, with example results presented. © 2006 IEEE.

Duke Scholars

Published In

IEEE Transactions on Signal Processing

DOI

ISSN

1053-587X

Publication Date

June 1, 2006

Volume

54

Issue

6 I

Start / End Page

2352 / 2356

Related Subject Headings

  • Networking & Telecommunications
 

Citation

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Dasgupta, N., & Carin, L. (2006). Texture analysis with variational hidden Markov trees. IEEE Transactions on Signal Processing, 54(6 I), 2352–2356. https://doi.org/10.1109/TSP.2006.872588
Dasgupta, N., and L. Carin. “Texture analysis with variational hidden Markov trees.” IEEE Transactions on Signal Processing 54, no. 6 I (June 1, 2006): 2352–56. https://doi.org/10.1109/TSP.2006.872588.
Dasgupta N, Carin L. Texture analysis with variational hidden Markov trees. IEEE Transactions on Signal Processing. 2006 Jun 1;54(6 I):2352–6.
Dasgupta, N., and L. Carin. “Texture analysis with variational hidden Markov trees.” IEEE Transactions on Signal Processing, vol. 54, no. 6 I, June 2006, pp. 2352–56. Scopus, doi:10.1109/TSP.2006.872588.
Dasgupta N, Carin L. Texture analysis with variational hidden Markov trees. IEEE Transactions on Signal Processing. 2006 Jun 1;54(6 I):2352–2356.

Published In

IEEE Transactions on Signal Processing

DOI

ISSN

1053-587X

Publication Date

June 1, 2006

Volume

54

Issue

6 I

Start / End Page

2352 / 2356

Related Subject Headings

  • Networking & Telecommunications