Analysis of multiscale texture segmentation using wavelet-domain hidden Markov models

Author

Choi, Hyeokho ; Hendricks, Brent ; Baraniuk, Richard

Author_Institution

Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX, USA

Volume

2

fYear

1999

fDate

24-27 Oct. 1999

Firstpage

1287

Abstract

Wavelet-domain hidden Markov tree (HMT) models are powerful tools for modeling the statistical properties of wavelet transforms. By characterizing the joint statistics of the wavelet coefficients, HMTs efficiently capture the characteristics of a large class of real-world signals and images. In this paper, we apply this multiscale statistical description to the texture segmentation problem. We also show how the Kullback-Leibler (KL) distance between texture models can provide a simple performance indicator.

Keywords

discrete wavelet transforms; hidden Markov models; image segmentation; image texture; quadtrees; statistical analysis; Kullback-Leibler distance; image texture segmentation; multiscale texture segmentation; real-world signals; statistical properties; wavelet transforms; wavelet-domain hidden Markov tree models; Discrete wavelet transforms; Hidden Markov models; Image segmentation; Pixel; Shape; Statistics; Time measurement; Wavelet analysis; Wavelet coefficients; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems, and Computers, 1999. Conference Record of the Thirty-Third Asilomar Conference on

Conference_Location

Pacific Grove, CA, USA

ISSN

1058-6393

Print_ISBN

0-7803-5700-0

Type

conf

DOI

10.1109/ACSSC.1999.831914

Filename

831914