Rabeeh Karimi Mahabadi

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We consider the class of convex minimization problems , composed of a self-concordant function, such as the log det metric, a convex data fidelity term h(·) and, a regularizing – possibly non-smooth – function g(·). This type of problems have recently attracted a great deal of interest, mainly due to their omnipres-ence in top-notch applications. Under this(More)
Despite the continuous advances in dense 3D surface reconstruction there are still many object classes which are a challenge for current algorithms. To tackle such classes shape priors have been proposed. One approach to shape priors is anisotropic surface regularization based on a prior knowledge about the shape [1]. A shape prior for a given object class(More)
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