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 language | English (US) |
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Title of host publication | Conference Record of the Asilomar Conference on Signals, Systems and Computers |
Editors | M.B. Matthews |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1692-1697 |
Number of pages | 6 |
Volume | 2 |
State | Published - 1998 |
Event | Proceedings of the 1998 32nd Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2) - Pacific Grove, CA, USA Duration: Nov 1 1998 → Nov 4 1998 |
Other
Other | Proceedings of the 1998 32nd Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2) |
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City | Pacific Grove, CA, USA |
Period | 11/1/98 → 11/4/98 |
ASJC Scopus subject areas
- Hardware and Architecture
- Signal Processing
- Electrical and Electronic Engineering