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Foreground detection is a challenging problem in complex scenes. In this paper, a novel foreground detection method is proposed which combines background subspace learning with object smoothing model. Considering background scenes in consecutive frames are almost the same, they are approximated using an efficient subspace learning technique which is based on 2D images. Due to the pixels of objects...
We study on the transmission of stereo videos over network. Because stereoscopic displays do not come into wide use, we need to separate the stereo video depending on the classification of views to satisfy different terminal displays when we transport the video over network. This transporting method of stereo video in Internet is called the view scalable transmission. But there comes one question,...
In this paper, we propose a novel dual pass video stabilization system using iterative motion estimation and adaptive motion smoothing. In the first pass, the transformation matrix to stabilize each frame is returned. The global motion estimation is carried out by a novel iterative method. The intentional motion is estimated using adaptive window smoothing. Before the beginning of the second pass,...
The Medium-Granular scalable (MGS) technologies in H.264/AVC-based scalable video coding (SVC) provide a flexible foundation to accommodate different network capacities. In order to make use of MGS in multiple environments conveniently, we need to obtain the Rate Distortion (R-D) function of SVC and design efficient bitstream extractions. In this paper, we proposed a simple and effective distortion...
White noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration, communication, and signal processing. Based on the optimal information fusion rules, in the linear minimum variance sense, three distributed optimal fused white noise deconvolution estimators weighted by matrices, diagonal matrices and scalars, are presented for the linear discrete...
For the multisensor linear discrete time-varying stochastic control systems with multi-model (different local models), three optimal weighted fusion Kalman smoothers weighted by matrices, diagonal matrices and scalars are presented in the linear minimum variance sense, respectively. They are locally optimal and are globally suboptimal. The accuracy of the fusers is higher than that of each local Kalman...
Based on the optimal information fusion rules weighted by matrices, diagonal matrices and scalars in the linear minimum variance sense, three distributed optimal fusion Kalman smoothers are presented for the linear discrete time-varying stochastic control systems with multisensor and colored measurement noises. Compared with the centralized fuser, they are locally optimal, but are globally suboptimal...
White noise deconvolution or input white noise estimation problem has important application background in oil seismic exploration. For the linear discrete time-varying stochastic control systems with multisensor and colored measurement noises, using the Kalman filtering method, under the optimal fusion weighted by matrices, diagonal matrices and scalars, optimal information fusion white noise deconvolution...
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