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

壓艙物

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.

空洞

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.

潛伏期

殘暴

和藹

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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