Two-Dimensional Hidden Markov Mesh Chain Algorithms for Image Dcoding


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 7, No. 6, pp. 1852-1860, Jun. 2000
10.3745/KIPSTE.2000.7.6.1852,   PDF Download:

Abstract

Distinct from the Markov random field or pseudo 2D HMM models for image analysis, this paper proposes a new model of 2D hidden Markov mesh chain(HMMM) model which subsumes the definitions of and the assumptions underlying the conventional HMM. The proposed model is a new theoretical realization of 2D HMM with the causality of top-down and left-right progression and the complete lattice constraint. These two conditions enable an efficient mesh decoding for model estimation and a recursive maximum likelihood estimation of model parameters. Those algorithms are developed in theoretical perspective and, in particular, the training algorithm, it is proved, attains the optimal set of parameters.


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Cite this article
[IEEE Style]
B. K. Sin, "Two-Dimensional Hidden Markov Mesh Chain Algorithms for Image Dcoding," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 7, no. 6, pp. 1852-1860, 2000. DOI: 10.3745/KIPSTE.2000.7.6.1852.

[ACM Style]
Bong Kee Sin. 2000. Two-Dimensional Hidden Markov Mesh Chain Algorithms for Image Dcoding. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 7, 6, (2000), 1852-1860. DOI: 10.3745/KIPSTE.2000.7.6.1852.