1. Texture analysis using gaussian weighted grey level co-occurrence probabilities
- Author
-
Rishi Jobanputra and David A. Clausi
- Subjects
Pixel ,business.industry ,Gaussian ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Pattern recognition ,Image segmentation ,Texture (geology) ,Weighting ,symbols.namesake ,Image texture ,symbols ,Probability distribution ,Segmentation ,Artificial intelligence ,business ,ComputingMethodologies_COMPUTERGRAPHICS ,Mathematics - Abstract
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 cooccurring 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.
- Published
- 2004