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Visual Data Mining Using the Point Distribution Tensor

Authors:
Marcel Ritter
Werner Benger
Biagio Cosenza
Keera Pullman
Hans Moritsch
Wolfgang Leimer

Keywords: metric tensor; scientific visualization; point cloud; OpenCL

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.

Pages: 199 to 202

Copyright: Copyright (c) IARIA, 2012

Publication date: February 29, 2012

Published in: conference

ISSN: 2308-4243

ISBN: 978-1-61208-184-7

Location: Saint Gilles, Reunion

Dates: from February 29, 2012 to March 5, 2012