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The success of sparse representation, in face recognition and visual tracking, has attracted much attention in computer vision in spite of its computational complexity. However, these sparse representation-based methods often assume that the coding residual follows either Gaussian or Laplacian distribution, which may not be precise enough to describe the coding residuals in real tracking situations...
The success of sparse representation in face recognition has motivated the development of sparse representation-based appearance models for visual tracking. These sparse representation-based trackers show state-of-the-art performance, but at the cost of computationally expensive l1-norm minimization. As the computational cost prevents the tracker from being used in real-time systems such as real-time...
The problem of estimating the exact shape of the glottal flow derivative (GFD) using reweighted 1-norm minimization of the second derivative of the GFD is addressed in this paper. By using physiological models of the glottal flow derivative, such as the Liljencrants-Fant (LF) and Rosenberg models, it is intuitively found that the second derivative of those models is highly sparse. Based on this observation...
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