Referenced Items are not available for this document.
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 fuse these decisions into a reliable pixel-by-pixel segmentation.
Published in:
Signals, Systems & Computers, 1998. Conference Record of the Thirty-Second Asilomar Conference on
(Volume:2
)
Date of Conference: 1-4 Nov. 1998
- Page(s):
- 1692 - 1697 vol.2
- Meeting Date :
- 01 Nov 1998-04 Nov 1998
- ISSN :
- 1058-6393
- Print ISBN:
- 0-7803-5148-7
- INSPEC Accession Number:
- 6330932
- Conference Location :
- Pacific Grove, CA, USA
- Digital Object Identifier :
- 10.1109/ACSSC.1998.751614
- Product Type:
- Conference Publications
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