Texture segmentation based on Markov random field model and multidirectional mosaics

Xiaoli Jiao; Wen Sheng

doi:10.1117/12.539026

25 September 2003 Texture segmentation based on Markov random field model and multidirectional mosaics

Xiaoli Jiao, Wen Sheng

Proceedings Volume 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition; (2003) https://doi.org/10.1117/12.539026
Event: Third International Symposium on Multispectral Image Processing and Pattern Recognition, 2003, Beijing, China

Abstract

This paper presents a texture segmentation approach based on Gauss-Markov random field(GMRF) model and multi-directional mosaics. Image texture is modeled by the second order GMRF model and the least error estimation is employed for the solution of model parameters. In order to improve the segmentation accuracy of uncertain area in boundary region between different textures, we introduced Laws energy masks and directional mosaics to obtain energy and orientation feature. And Euclidean distance approach is employed to classify different features. Experiments show that accuracy of texture segmentation can be improved.

Citation Download Citation

Xiaoli Jiao and Wen Sheng "Texture segmentation based on Markov random field model and multidirectional mosaics", Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); https://doi.org/10.1117/12.539026

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