|Title||Texture analysis using Gaussian weighted grey level co-occurrence probabilities|
|Publication Type||Conference Paper|
|Year of Publication||2004|
|Authors||Jobanputra, R., and D. A. Clausi|
|Conference Name||1st Canadian Conference on Computer and Robot Vision|
|Conference Location||London, Ontario, Canada|
|Keywords||Grey level co-occurrence, probabilities, remote sensing imagery, SAR sea ice, Segmentation, texture analysis, texture features|
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.
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.