@article {438,
	title = {Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {42},
	year = {2004},
	pages = {215 - 228},
	abstract = {<p>This paper compares the discrimination ability of two texture analysis methods: Markov random fields (MRFs) and gray-level cooccurrence probabilities (GLCPs). There exists limited published research comparing different texture methods, especially with regard to segmenting remotely sensed imagery. The role of window size in texture feature consistency and separability as well as the role in handling of multiple textures within a window are investigated. Necessary testing is performed on samples of synthetic (MRF generated), Brodatz, and synthetic aperture radar (SAR) sea ice imagery. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, which is important when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than MRFs given their respective segmentation procedures.</p>
},
	keywords = {Brodatz, discrimination ability, Feature Extraction, GLCP, gray-level cooccurrence probabilities, image segmentation, image texture, Markov processes, Markov Random Fields, MRF, Remote Sensing, remote sensing by radar, SAR sea ice imagery, sea ice, synthetic aperture radar, texture analysis, texture boundary, texture feature consistency, texture feature separability, texture methods, window size},
	issn = {0196-2892},
	doi = {http://dx.doi.org/10.1109/TGRS.2003.817218},
	author = {D A. Clausi and B Yue}
}
@inproceedings {627,
	title = {Texture segmentation comparison using grey level co-occurrence probabilities and Markov random fields},
	booktitle = {17th International Conference on Pattern Recognition (ICPR)},
	year = {2004},
	month = {08/2004},
	address = {Cambridge, United Kingdom},
	abstract = {<p>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.</p>
},
	keywords = {discrimination ability, Feature Extraction, Gaussian Markov random fields, Gaussian processes, grey level co-occurrence probabilities, image segmentation, image texture, Markov processes, probability, texture features, texture segmentation},
	doi = {http://dx.doi.org/10.1109/ICPR.2004.1334208},
	author = {D A. Clausi and B Yue}
}
