NDLI: Unsupervised texture segmentation based on the modified Markov random field model

Content Provider	IEEE Xplore Digital Library
Author	Xiaohan, Y. Yla-Jaaski, J.
Copyright Year	1992
Description	Author affiliation: Graphic Arts Lab., Tech. Res. Centre of Finland, Espoo, Finland (Xiaohan, Y.; Yla-Jaaski, J.)
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.
Starting Page	88
Ending Page	91
File Size	315259
Page Count	4
File Format	PDF
ISBN	0818629207
DOI	10.1109/ICPR.1992.201934
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1992-08-30
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov random fields Image segmentation Gaussian processes Stability Predictive models Decorrelation Computer vision Statistical distributions Parameter estimation Image edge detection
Content Type	Text
Resource Type	Article

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Unsupervised texture segmentation based on the modified Markov random field model