A Computationally Efficient Approach to the Estimation of
Two- and Three-dimensional Hidden Markov Models
Dhiraj Joshi, Jia Li, James Z. Wang
The Pennsylvania State University, University Park, PA 16802
Statistical modeling methods are becoming indispensable in today's
large-scale image analysis. In this paper, we explore a
computationally efficient parameter estimation algorithm for two and
three dimensional hidden Markov models and show applications to
satellite image segmentation. The proposed parameter estimation
algorithm is compared with the first proposed algorithm for 2-D HMMs
based on variable state Viterbi. We also propose a 3-D hidden Markov
model (3-D HMM) for volume image modeling and apply it to volume image
segmentation using a large number of synthetic images with ground
truth. Experiments have demonstrated the computational efficiency of
the proposed parameter estimation technique for 2-D HMMs and a
potential of 3-D HMM as a stochastic modeling tool for volume images.
(high resolution PDF, 5MB)
Dhiraj Joshi, Jia Li and James Z. Wang, ``A Computationally Efficient Approach to the Estimation of Two- and Three-
dimensional Hidden Markov Models,'' IEEE Transactions on
Image Processing, vol. 15, no. 7, pp. 1871-1886, 2006.
Copyright 2006 IEEE.
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October 5, 2005