We explore a novel algorithm to analyze arbitrary distributions of 3D-points. Using a direct tensor field visualization technique allows to easily identify regions of linear, planar or isotropic structure. This approach is very suitable for visual data mining and exemplified upon geoscience applications. It allows to distinguish, for example, power lines and flat terrains in LIDAR scans. We furthermore present the work on the optimization of the computationally intensive algorithm using OpenCL and potentially utilizing the Insieme optimizing compiler framework.
Visual Data Mining Using the Point Distribution Tensor
COSENZA B;
2012-01-01
Abstract
We explore a novel algorithm to analyze arbitrary distributions of 3D-points. Using a direct tensor field visualization technique allows to easily identify regions of linear, planar or isotropic structure. This approach is very suitable for visual data mining and exemplified upon geoscience applications. It allows to distinguish, for example, power lines and flat terrains in LIDAR scans. We furthermore present the work on the optimization of the computationally intensive algorithm using OpenCL and potentially utilizing the Insieme optimizing compiler framework.File in questo prodotto:
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