Coupling Reduced Models for Optimal Motion Estimation

被引:0
|
作者
Drifi, Karim [1 ]
Herlin, Isabelle [1 ]
机构
[1] INRIA, Rennes, France
来源
2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012) | 2012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
The paper discusses the issue of motion estimation by image assimilation in numerical models, based on Navier-Stokes equations. In such context, models' reduction is an attractive approach that is used to decrease cost in memory and computation time. A reduced model is obtained from a Galerkin projection on a subspace, defined by its orthogonal basis. Long temporal image sequences may then be processed by a sliding-window method. On the first sub-window, a fixed basis is considered to define the reduced model. On the next ones, a Principal Order Decomposition is applied, in order to define a basis that is simultaneously small-size and adapted to the studied image data. Results are given on synthetic data and quantified according to state-of-the-art methods. Application to satellite images demonstrates the potential of the approach.
引用
收藏
页码:2651 / 2654
页数:4
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