Titlebook: Applications of Discrete Geometry and Mathematical Morphology; First International Ullrich Köthe,Annick Montanvert,Pierre Soille Conferenc

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Shuttle

Machine Learning as a Preprocessing Phase in Discrete Tomography,hine learning can be used in . as a preprocessing step in order to choose the proper reconstruction algorithm or – with the aid of the knowledge acquired – to improve its accuracy. We also show how to design new evolutionary reconstruction methods that can exploit the information gained by machine learning classifiers.

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Fast Planarity Estimation and Region Growing on GPU,. This region growing is then further optimized using a parallel implementation. The sparse nature of these filters and the inherent parallelism of the filter bank allow a fast implementation on a parallel processor architecture such as the Compute Unified Device Architecture (CUDA) from nVIDIA.

潜伏期

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Gingivitis

Advancements in YARN Resource Managerf desired constraints are satisfied. The framework of ultrametric watersheds provides a generic scheme for computing any hierarchical connected clustering, in particular when such a hierarchy is constrained. The suitability of this framework for solving practical problems is illustrated with applications in remote sensing.

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