Parallel Hidden Hierarchical Fields for Multi-scale Reconstruction

被引：0
|
作者：
Liu, Ying [1 ]
Fieguth, Paul [1 ]
机构：
[1] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada
来源：
ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS | 2009年 / 5681卷
关键词：
SEGMENTATION; RELAXATION; MODEL;
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 any problem involving images having scale-dependent structures, a key issue is the modeling of these multi-scale characteristics. Because multi-scale phenomena frequently possess nonstationary, piece-wise multi-model behaviour, the classic hidden Markov method can not perform well in modeling such complex images. In this paper we provide a new modeling approach to extend previous hierarchical methods, with multiple hidden fields, to perform reconstruction in more complex, nonstationary contexts.
引用
收藏
页码：70 / 83
页数：14
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