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Proceedings Paper

Fractal model for digital image texture analysis
Author(s): Michael G. Petrolekas; Sunanda Mitra
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Paper Abstract

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.

Paper Details

Date Published: 12 January 1993
PDF: 7 pages
Proc. SPIE 1771, Applications of Digital Image Processing XV, (12 January 1993); doi: 10.1117/12.139073
Show Author Affiliations
Michael G. Petrolekas, Texas Tech Univ. (United States)
Sunanda Mitra, Texas Tech Univ. (United States)


Published in SPIE Proceedings Vol. 1771:
Applications of Digital Image Processing XV
Andrew G. Tescher, Editor(s)

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