Unsupervised multiscale focused objects detection using hidden Markov tree

被引：0
|
作者：
Ye, Z [1 ]
Lu, CC [1 ]
机构：
[1] Kent State Univ, Dept Comp Sci, Kent, OH 44242 USA
来源：
PROCEEDINGS OF THE 6TH JOINT CONFERENCE ON INFORMATION SCIENCES | 2002年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we introduce an unsupervised statistical approach to segment sharply focused objects in low depth of field (DOF) images using the Hidden Markov Tree (HMT) model which is based on the wavelet domain. A multiscale statistical description of high frequency wavelet coefficients is applied to segment the focused objects from background and foreground Using the Hybrid Contextual Labeling Tree (HCLT) model, more than one context is also considered in the interscale fusion algorithm. Experiments have shown that both accurate and reliable results are obtained.
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页码：812 / 815
页数：4
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