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Proceedings Paper

Feature extraction from ladar data using modified GPCA
Author(s): Peter F. Stiller
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Paper Abstract

In this paper we present a method for extracting feature information from ladar data presented in the form of a spatial point cloud. The method exploits a modified version of Generalized Principal Component Analysis (GPCA) to extract planar, or even non-linear, surface elements from this sort of data. The essential difficulty is that, depending on the aspect of the object, certain surfaces will be minimally exposed. As a result we cannot say in advance how many surfaces we are looking for, and we cannot reliably detect surfaces that are hit by only a few of the points in the cloud. An additional difficulty occurs when reconstructing the surface normal at points near where two surfaces join. The algorithm handles both issues and captures enough essential surface features to allow accurate alignment to say a CAD model for detailed recognition. One can also use the extracted planar facets as a kind of partial bounding polyhedron (modified partial bounding box) as input to an initial identification algorithm that works off of the invariants of the planar arrangement.

Paper Details

Date Published: 2 February 2012
PDF: 8 pages
Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 829509 (2 February 2012); doi: 10.1117/12.910221
Show Author Affiliations
Peter F. Stiller, Texas A&M Univ. (United States)


Published in SPIE Proceedings Vol. 8295:
Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II
Karen O. Egiazarian; John Recker; Guijin Wang; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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