Abstract

The two-point probability density function (2P-PDF) gives a full description of the first- and second-order statistics of a random process. We propose a framework for texture classification based on a distance measure between 2P-PDF’s after equalization of first-order statistics. This framework allows extraction of the structural information of the process independently of the dynamic range of the image. We present two methods for estimating the 2P-PDF of texture images, and we establish some criteria for efficient computation. The theoretical framework for noise-free texture images is validated with four texture ensembles.

© 1999 Optical Society of America

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