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Many practical problems require estimating future values of a Positive Definite matrix from past historical data. While several models have been proposed in the literature for propagating past data in this context, the problem of identifying these models from experiments is largely open. The main result of this paper is an efficient convex optimization based algorithm for identifying a class of models...
In this paper we present a method to segment RGBD image of a scene into coherent and meaningful parts using both the appearance features and depth information. The segmentation method is totally based on graph cuts theory which uses our proposed unsupervised Conditional Random Field (CRF) model. We evaluate our method both quantitatively and qualitatively on a set of RGBD images of NYU dataset. The...
In practice, the average time of (deterministic or randomized) sorting algorithms seems to be more relevant than the worst case time of deterministic algorithms. Still, the many known complexity bounds for parallel comparison sorting include no nontrivial lower bounds for the average time required to sort by comparisons n elements with p processors (via deterministic or randomized algorithms). We...
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