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
Alexei A. Goon and Jannick P. Rolland, "Texture classification based on comparison of second-order statistics. I. Two-point probability density function estimation and distance measure," J. Opt. Soc. Am. A 16, 1566-1574 (1999)