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

Hyeokho Choi, Brent Hendricks, Richard Baraniuk

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

2 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. We also show how the Kullback-Leibler (KL) distance between texture models can provide a simple performance indicator.

Original languageEnglish (US)
Title of host publicationConference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers
EditorsMichael B. Matthews
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1287-1291
Number of pages5
Volume2
ISBN (Electronic)0780357000, 9780780357006
DOIs
StatePublished - Jan 1 1999
Event33rd Asilomar Conference on Signals, Systems, and Computers, ACSSC 1999 - Pacific Grove, United States
Duration: Oct 24 1999Oct 27 1999

Other

Other33rd Asilomar Conference on Signals, Systems, and Computers, ACSSC 1999
CountryUnited States
CityPacific Grove
Period10/24/9910/27/99

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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