The present paper uses a fractal model for differentiating and quantifying image texture. The employment of the fractal model to texture classification involves evaluation of the fractal dimension of the images concerned. A parametric representation of the image texture in terms of fractal dimension is achieved by extending fractional Brownian motion to the discrete case and using a maximum likelihood estimator (MLE) for estimation of the fractal parameter H. The algorithm developed for this model is applied successfully to texture classification of synthetic polymeric membranes. Such texture classification provides us with a quantitative descriptor of polymeric membrane morphology for establishing a correlation between the morphology and the chemical transport phenomena in generating membranes for various industrial applications.
|Number of pages||7|
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|State||Published - Jan 12 1993|
|Event||Applications of Digital Image Processing XV 1992 - San Diego, United States|
Duration: Jul 22 1992 → …