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In this paper, we propose a novel $L_{0}$ -regularized optimization framework for image downscaling. The optimization is driven by two $L_{0}$ -regularized priors. The first prior, gradient-ratio prior, is based on the observation that the number of edges in the downscaled image is approximately inverse square proportional to the downscaling factor. By introducing $L_{0}$ norm sparsity to the...
Interest points are widely used in computer vision applications such as camera calibration, robot localization and object tracking that require fast and efficient feature matching. A large number of techniques have been proposed in the literature. This paper evaluates the state of art techniques for interest point detection including execution time and suitability for real time applications. Such...
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