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On context-based Bayesian image segmentation: joint multi-context and multiscale approach and wavelet-domain hidden Markov models

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2 Author(s)
Guoliang Fan ; Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA ; Xiang-Gen Xia

We show that context-based Bayesian image segmentation can be improved by strengthening both contextual modeling and statistical texture characterization. Firstly, we develop a joint multi-context and multiscale segmentation algorithm to achieve more robust contextual modeling by using multiple context models. Secondly, we study statistical texture characterization using wavelet-domain hidden Markov models (HMMs), and in particular, we use an improved HMM, HMT-3S to obtain more accurate multiscale texture characterization. Experimental results on two synthetic mosaic show that both contextual modeling and texture characterization play important roles in context-based Bayesian image segmentation.

Published in:

Signals, Systems and Computers, 2001. Conference Record of the Thirty-Fifth Asilomar Conference on  (Volume:2 )

Date of Conference:

4-7 Nov. 2001