Abstract
This work addresses the two major drawbacks of current statistical uncertain geometric reasoning approaches. In the first part a framework is presented, that allows to represent uncertain line segments in 2D- and 3D-space and perform statistical test with these practically very important types of entities. The second part addresses the issue of performance of geometric reasoning. A data structure is introduced, that allows the efficient processing of large amounts of statistical tests involving geometric entities. The running times of this approach are finally evaluated experimentally.
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Beder, C. (2004). Fast Statistically Geometric Reasoning About Uncertain Line Segments in 2D- and 3D-Space. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_46
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DOI: https://doi.org/10.1007/978-3-540-28649-3_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22945-2
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