Title

Texture Classification Based On Comparison Of Second-Order Statistics. I. Two-Point Probability Density Function Estimation And Distance Measure

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.

Publication Date

1-1-1999

Publication Title

Journal of the Optical Society of America A: Optics and Image Science, and Vision

Volume

16

Issue

7

Number of Pages

1566-1574

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1364/JOSAA.16.001566

Socpus ID

0032607096 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0032607096

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