Maximum likelihood texture classification and Bayesian texture segmentation using discrete wavelet frames

Author

Liapis, S. ; Alvertos, N. ; Tziritas, G.

Author_Institution

Dept. of Comput. Sci., Crete Univ., Heraklion, Greece

Volume

2

fYear

1997

fDate

2-4 Jul 1997

Firstpage

1107

Abstract

A new approach is presented for the classification and segmentation of texture images, where a different statistical methodology and criterion for texture characterization is proposed. The scheme, in both problems, uses the concept of discrete wavelet frames for the appropriate frequency decompositions, as applied to 2-D signals, and a distance measure based on the evaluation of parametric scatter matrices of the texture images to be segmented or classified. Experiments yielding excellent results are presented for both algorithms

Keywords

Bayes methods; image classification; image segmentation; image texture; matrix algebra; maximum likelihood estimation; wavelet transforms; 2D signals; Bayesian texture segmentation; algorithms; discrete wavelet frames; distance measure; experiments; frequency decomposition; image classification; image segmentation; maximum likelihood texture classification; parametric scatter matrices; statistical method; texture characterization; texture images; Bayesian methods; Computer science; Discrete wavelet transforms; Electronic mail; Filters; Frequency domain analysis; Frequency measurement; Image segmentation; Image texture analysis; Statistical analysis;

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.1997.628559

Filename

628559