@inproceedings {591,
	title = {Adaptive color image segmentation using Markov random fields},
	booktitle = {International Conference on Pattern Recognition},
	year = {2002},
	address = {Rochester, NY},
	abstract = {<p>A new framework for color image segmentation is introduced generalizing the concepts of point-based and spatially-based methods. This framework is based on Markov random fields using a continuous Gibbs sampler. The Markov random fields approach allows for a rigorous computational framework where local and global spatial constraints can be globally optimized. Using a continuous Gibbs sampler enables the algorithm to adapt continuous-valued regional prototypes in a manner analogous to vector quantization while the discrete Gibbs sampler is used to adjust region boundaries.</p>
},
	keywords = {adaptive color image segmentation, adaptive signal processing, continuous Gibbs sampler, continuous-valued regional prototypes, discrete Gibbs sampler, global spatial constraints, image colour analysis, image sampling, image segmentation, local spatial constraints, Markov processes, Markov Random Fields, point-based methods, random processes, region boundaries adjustment, spatially-based methods, vector quantization},
	doi = {http://dx.doi.org/10.1109/ICIP.2002.1039085},
	author = {S Wesolkowski and P Fieguth}
}
@article {575,
	title = {The effect of speckle filtering on scale-dependent texture estimation of a forested scene},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {38},
	year = {2000},
	pages = {1160 - 1170},
	abstract = {<p>Spatial fluctuations in microwave backscatter may be an important piece of information in discriminating tree stands. However, the presence of speckle in synthetic aperture radar (SAR) image data is a barrier to the exploitation of image texture. The authors explored a new methodology that combines a recent adaptive speckle reduction algorithm by Lopes et al. (1990) with a generic texture estimation scheme. They investigated the claim that this filter was capable of preserving backscatter texture. To understand if speckle reduction was destroying backscatter texture, they compared the strength of the relationship between forest inventory parameters and image texture as a function of spatial scale for both filtered and unfiltered images. They used Radarsat Fine mode image data: single look resolution is approximately 8.5 m, and pixel spacing is 3 m. Their study area was northern Vancouver Island, B.C., on the west coast of Canada. For the unfiltered data, they found that the ability of image texture to predict the forest parameters decreased as the texture scale increased from 3 to 13 m, suggesting greater information content in the small scale texture. For the filtered data, this relationship was much weaker at small scales and was not a function of distance. Their results suggest that the speckle filter was not retaining small scale texture, which is consistent with the theoretical hypotheses underlying its multiplicative noise model. They also show that there is significant information in small state SAR image texture that may be used as an adjunct to other spatial information for discriminating tree stands in the temperate rain forest</p>
},
	keywords = {adaptive signal processing, adaptive speckle reduction algorithm, backscatter texture, forest, forested scene, forestry, generic texture estimation scheme, geophysical measurement technique, geophysical signal processing, geophysical techniques, image texture, land surface, microwave backscatter, radar imaging, radar remote sensing, remote sensing by radar, SAR, scale-dependent texture estimation, spatial fluctuations, speckle, speckle filtering, synthetic aperture radar, tree stand, vegetation mapping},
	issn = {0196-2892},
	author = {M J. Collins and J Wiebe and D A. Clausi}
}
