Multiscale texture segmentation using wavelet-domain hidden Markov models

Hyeokho Choi, Richard Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

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. Using the inherent tree structure of the HMT, we classify textures at various scales and then use these decisions into a reliable pixel-by-pixel segmentation.

Original languageEnglish (US)
Title of host publicationConference Record of the Asilomar Conference on Signals, Systems and Computers
EditorsM.B. Matthews
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1692-1697
Number of pages6
Volume2
StatePublished - 1998
EventProceedings of the 1998 32nd Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2) - Pacific Grove, CA, USA
Duration: Nov 1 1998Nov 4 1998

Other

OtherProceedings of the 1998 32nd Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2)
CityPacific Grove, CA, USA
Period11/1/9811/4/98

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Electrical and Electronic Engineering

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