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.

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Citation: 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.

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Last Modified: October 5, 2005