Unsupervised texture segmentation based on the modified Markov random field model

Author

Xiaohan, Yu ; Ylä-Jääski, Juha

Author_Institution

Graphic Arts Lab., Tech. Res. Centre of Finland, Espoo, Finland

fYear

1992

fDate

30 Aug-3 Sep 1992

Firstpage

88

Lastpage

91

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

Keywords

Markov processes; correlation methods; image processing; image segmentation; Gaussian-Markov random field model; data compression; decorrelation; feature extraction; image processing; image segmentation; support region; unsupervised texture segmentation; Computer vision; Decorrelation; Gaussian processes; Image edge detection; Image segmentation; Markov random fields; Parameter estimation; Predictive models; Stability; Statistical distributions;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1992. Vol.III. Conference C: Image, Speech and Signal Analysis, Proceedings., 11th IAPR International Conference on

Conference_Location

The Hague

Print_ISBN

0-8186-2920-7

Type

conf

DOI

10.1109/ICPR.1992.201934

Filename

201934