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

Dhiraj Joshi, Jia Li, James Z. Wang

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1871-1886
Number of pages16
JournalIEEE Transactions on Image Processing
Volume15
Issue number7
DOIs
StatePublished - Jul 1 2006

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

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