Texture characterization via joint statistics of wavelet coefficient magnitudes

Author

Simoncelli, Eero P. ; Portilla, Javier

Author_Institution

Courant Inst. of Math. Sci., New York Univ., NY, USA

Volume

1

fYear

1998

fDate

4-7 Oct 1998

Firstpage

62

Abstract

We present a parametric statistical characterization of texture images in the context of an overcomplete complex wavelet frame. The characterization consists of the local autocorrelation of the coefficients in each subband, the local autocorrelation of the coefficent magnitudes, and the cross-correlation of coefficient magnitudes at all orientations and adjacent spatial scales. We develop an efficient algorithm for sampling from an implicit probability density conforming to these statistics, and demonstrate its effectiveness in synthesizing artificial and natural texture images

Keywords

correlation methods; image sampling; image texture; statistical analysis; wavelet transforms; artificial texture images; coefficent magnitudes; cross-correlation; implicit probability density; joint statistics; local autocorrelation; natural texture images; overcomplete complex wavelet frame; parametric statistical characterization; sampling; texture characterization; texture images; wavelet coefficient magnitudes; Adaptive filters; Application software; Autocorrelation; Computer vision; Histograms; Image sampling; Optical filters; Parametric statistics; Probability; Wavelet coefficients;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on

Conference_Location

Chicago, IL

Print_ISBN

0-8186-8821-1

Type

conf

DOI

10.1109/ICIP.1998.723417

Filename

723417