Unsupervised texture segmentation based on the modified Markov random field model

Y Xiaohan,J Yla-Jaaski

doi:10.1109/icpr.1992.201934

Unsupervised texture segmentation based on the modified Markov random field model

Y Xiaohan, J Yla-Jaaski

https://doi.org/10.1109/icpr.1992.201934

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Publication Date: Aug 30, 1992

Citations: 3

#Random Field Model #MRF Model + Show 8 more

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Abstract

The Gaussian-Markov random field (MRF) model is a very useful technique for image processing, such as feature extraction and data compression. However its strict stability condition makes the model identification complex. The major problem is the choice of a proper support region for the model. In this paper a new model is proposed which is based on the MRF model and called the modified Gaussian-Markov random field model. It is not an optimal MRF model but has a very useful property, namely decorrelation. A stable modified MRF model always exists even if a stable MRF model does not exist on the given support region. Applications to texture segmentation are also presented. >

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