Parameter estimation is a key computational issue in all statistical image modeling techniques. In this paper, we explore a computationally efficient parameter estimation algorithm for multi-dimensional hidden Markov models. 2-D HMM has been applied to supervised aerial image classification and comparisons have been made with the first proposed estimation algorithm. An extensive parametric study has been performed with 3-D HMM and the scalability of the estimation algorithm has been discussed. Results show the great applicability of the explored algorithm to multi-dimensional HMM based image modeling applications.
Parameter estimation of multi-dimensional hidden Markov models - a scalable approach
IEEE International Conference on Image Processing 2005 ; 3 ; III-149
2005-01-01
271343 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Parameter Estimation of Multi-Dimensional Hidden Markov Models - A Scalable Approach
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