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

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3 Author(s)
D. Joshi ; Dept. of Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA, USA ; Jia Li ; J. Z. Wang

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.

Published in:

IEEE Transactions on Image Processing  (Volume:15 ,  Issue: 7 )