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

Content Provider	IEEE Xplore Digital Library
Author	Clausi, D.A. Bing Yue
Copyright Year	2004
Description	Author affiliation: Dept. of Syst. Design Eng., Waterloo Univ., Ont., Canada (Clausi, D.A.; Bing Yue)
Abstract	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.
Starting Page	584
Ending Page	587
File Size	412767
Page Count	4
File Format	PDF
ISBN	0769521282
ISSN	10514651
DOI	10.1109/ICPR.2004.1334208
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-26
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov random fields Image segmentation Testing Probability Design engineering Systems engineering and theory Feature extraction Synthetic aperture radar Sea ice Statistics
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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