Titlebook: Energy Minimization Methods in Computer Vision and Pattern Recognition; 11th International C Marcello Pelillo,Edwin Hancock Conference proc

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https://doi.org/10.1007/1-4020-3842-9inimization approaches first establish a correspondence of the current frame to all its neighbors in some radius and then use this temporal information for enhancement. In this paper, we propose the first variational super resolution approach that computes several super resolved frames in one batch

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https://doi.org/10.1007/978-3-642-80249-2tion of a passive scalar transported by a fluid flow. The Eulerian fluid flow velocity field is decomposed into two components: a large-scale motion field and a small-scale uncertainty component. We define the small-scale component as a random field. Then the data term of the optical flow formulatio

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https://doi.org/10.1007/978-3-476-02778-8ral computational unit processes large amounts of data in real time that is provided by distributed cameras. High network traffic, cost of storage on the central unit, scalability of the system, and vulnerability of the central unit to attacks are among the disadvantages of such systems. In this pap

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Das Gespräch über Literatur im Unterrichtmage sequence and a candidate latent image restoration. Minimizing the functional using the alternating direction method of multipliers (ADMM) and Moreau proximity mapping leads to a general algorithmic flow. We show that various known algorithms can be derived as special cases of the general approa

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https://doi.org/10.1007/978-3-319-78199-0artificial intelligence; clustering; clustering analysis; computer vision; estimation; image analysis; ima