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CVPR
2009
IEEE

Learning Visual Flows: A Lie Algebraic Approach

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Learning Visual Flows: A Lie Algebraic Approach
We present a novel method for modeling dynamic visual phenomena, which consists of two key aspects. First, the in- tegral motion of constituent elements in a dynamic scene is captured by a common underlying geometric transform pro- cess. Second, a Lie algebraic representation of the trans- form process is introduced, which maps the transformation group to a vector space, and thus overcomes the difficul- ties due to the group structure. Consequently, the statis- tical learning techniques based on vector spaces can be readily applied. Moreover, we discuss the intrinsic con- nections between the Lie algebra and the Linear dynamical processes, showing that our model induces spatially vary- ing fields that can be estimated from local motions without continuous tracking. Following this, we further develop a statistical framework to robustly learn the flow models from noisy and partially corrupted observations. The proposed methodology is demonstrated on real world phenomenon,...
Dahua Lin, W. Eric L. Grimson, John W. Fisher III
Added 09 May 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Dahua Lin, W. Eric L. Grimson, John W. Fisher III
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