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- W2128201669 abstract "This paper presents a novel formulation for object tracking. We model the second-order statistics of image regions and perform covariance matching under the variational level set framework. Specifically, covariance matrix is adopted as a visual object representation for partial differential equation (PDE) based contour tracking. Log-Euclidean calculus is used as a covariance distance metric instead of Euclidean distance which is unsuitable for measuring the similarities between covariance matrices, because the matrices typically lie on a non-Euclidean manifold. A novel image energy functional is formulated by minimizing the distance metrics between the candidate object region and a given template, and maximizing the ones between the background region and the template. The corresponding gradient flow is then derived according to a variational approach, enabling PDE-based visual tracking. Experiments on synthetic and real video sequences prove the validity of the proposed method." @default.
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- W2128201669 date "2011-08-01" @default.
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- W2128201669 title "Covariance Matching for PDE-based Contour Tracking" @default.
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- W2128201669 doi "https://doi.org/10.1109/icig.2011.88" @default.
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