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Texture segmentation comparison using grey level co-occurrence probabilities and Markov random fields

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2 Author(s)
Clausi, D.A. ; Dept. of Syst. Design Eng., Waterloo Univ., Ont., Canada ; Bing Yue

The discrimination ability of texture features derived from Gaussian Markov random fields (GMRFs) and grey level co-occurrence probabilities (GLCPs) are compared and contrasted. More specifically, the role of window size in feature consistency and separability as well as the role of multiple textures within a window are investigated. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, an important concept when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than GMRFs.

Published in:

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on  (Volume:1 )

Date of Conference:

23-26 Aug. 2004