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IEEE Trans Image Process. 2006 Jul;15(7):1871-86.

A computationally efficient approach to the estimation of two- and three-dimensional hidden Markov models.

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Department of Computer Science and Engineering, The Pennsylvania State University, University Park 16802, USA.


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-dimensional (2-D) and three-dimensional (3-D) hidden Markov models (HMMs) 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 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.

[Indexed for MEDLINE]

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