@inproceedings {443,
	title = {Texture analysis using Gaussian weighted grey level co-occurrence probabilities},
	booktitle = {1st Canadian Conference on Computer and Robot Vision},
	year = {2004},
	address = {London, Ontario, Canada},
	abstract = {<p>The discrimination of textures is a significant aspect in segmenting SAR sea ice imagery. Texture features calculated from grey level co-occurring probabilities (GLCP) are well accepted and applied in the analysis of many images. When calculating GLCPs, each co-occurring pixel pair within the image window is given a uniform weighting. Although a novel technique, co-occurring texture features have a tendency to misclassify and erode texture boundaries due to the large window sizes needed to capture meaningful statistics.</p>

<p>A method is proposed whereby co-occurring pixel pairs closer to the center of the image window are assigned larger co-occurring probabilities according to a Gaussian distribution. By using a Gaussian weighting scheme to calculate the GLCPs, less significance is given to pixel pairs that are on the outlying regions of the window, which have a tendency to produce erroneous statistics as the image window overlaps a texture boundary. This method proves to preserve the edge strength between textures and provides better segmentation at the expense of computational complexity.</p>
},
	keywords = {Grey level co-occurrence, probabilities, remote sensing imagery, SAR sea ice, Segmentation, texture analysis, texture features},
	doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301421},
	author = {R Jobanputra and D A. Clausi}
}
