ICIP 2006, Atlanta, GA

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Paper Detail

Session:Image and Video Filtering
Time:Tuesday, October 10, 09:40 - 12:20
Presentation: Poster
Topic: Image & Video Filtering: Other
Authors: Junlan Yang; University of Illinois at Chicago 
 Dan Schonfeld; University of Illinois at Chicago 
 Chong Chen; University of Illinois at Chicago 
 Magdi Mohamed; Motorola Labs 
Abstract: Particle filters have been introduced as a powerful tool to estimate the posterior density of nonlinear systems. These filters are also capable of processing data online as required in many practical applications. In this paper, we propose a novel technique for video stabilization based on the particle filtering framework. Scale-invariant feature points are extracted to form a rough estimate which is used to model the importance density. We use a constant-velocity Kalman filter model to estimate intentional camera movement. We also prove that the particle filtering estimate will lower the error variance. The superior performance and robustness of our algorithm is demonstrated by computer simulations.