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
APA
Chicago
ICMJE
MLA
NLM
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