Multiscale texture segmentation using wavelet-domain hidden Markov models

被引：0
|
作者：
Choi, HK [1 ]
Baraniuk, R [1 ]
机构：
[1] Rice Univ, Dept Elect & Comp Engn, Houston, TX 77005 USA
来源：
CONFERENCE RECORD OF THE THIRTY-SECOND ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, VOLS 1 AND 2 | 1998年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：1692 / 1697
页数：6
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