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Proceedings Paper

A statistical analysis of 3D structure tensor features generated from LADAR imagery
Author(s): Miguel Ordaz; Estille Whittenberger; Donald Waagen; Donald Hulsey
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Paper Abstract

Extraction and efficient representation of informative structure from data is the goal of pattern recognition. Efficient and effective parametric and nonparametric representations for capturing the geometry of three-dimensional objects are an area of current research. Tang and Medioni have proposed tensor representations for characterization and reconstruction of surfaces. 3-D structure tensors are extracted by mapping surface geometries using a rank-2 covariant tensor. Distributional differences between representations of objects of interest can (theoretically) be used for target matching and identification. This paper analyzes the statistical distributions of tensor representation extracted from 3-D LADAR imagery and quantifies a measure of divergence between images of three vehicles as a function of tensor feature support size.

Paper Details

Date Published: 18 May 2006
PDF: 12 pages
Proc. SPIE 6234, Automatic Target Recognition XVI, 623408 (18 May 2006); doi: 10.1117/12.673389
Show Author Affiliations
Miguel Ordaz, Raytheon Missile Systems (United States)
Estille Whittenberger, Raytheon Missile Systems (United States)
Donald Waagen, Raytheon Missile Systems (United States)
Donald Hulsey, Raytheon Missile Systems (United States)

Published in SPIE Proceedings Vol. 6234:
Automatic Target Recognition XVI
Firooz A. Sadjadi, Editor(s)

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