Binomial linear predictive approach to texture modeling

Author

Becchetti, Claudio ; Campisi, Patrizio

Author_Institution

Infocom Dept., Rome Univ., Italy

Volume

2

fYear

1997

fDate

2-4 Jul 1997

Firstpage

1111

Abstract

We present a parametric texture analysis and synthesis method based on a sparse autoregressive model. The method is aimed at reducing the number of parameters necessary to define the stochastic behavior of a random field representing a given texture. For this purpose, it systematically selects the dominant contributions to the pixel cross-correlation. The method is employed in the first stage of a nonlinear texture model. Dimensional and computational advantages as well as application limits are discussed

Keywords

autoregressive processes; correlation methods; feature extraction; image texture; linear systems; parameter estimation; prediction theory; random processes; binomial linear predictive approach; computational advantages; feature extraction; nonlinear texture model; parametric texture analysis; parametric texture synthesis method; pixel cross-correlation; random field; sparse autoregressive model; stochastic behavior; texture modeling; Autocorrelation; Bit rate; Context modeling; Electronic mail; Minimax techniques; Predictive models; Signal synthesis; Statistical analysis; Stochastic processes; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing Proceedings, 1997. DSP 97., 1997 13th International Conference on

Conference_Location

Santorini

Print_ISBN

0-7803-4137-6

Type

conf

DOI

10.1109/ICDSP.1997.628560

Filename

628560