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Proceedings Paper

Using non-negative matrix factorization toward finding an informative basis in spin-image data
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Paper Abstract

Three-dimensional (3D) Laser Detection and Ranging (LADAR) range data is being investigated for automatic target recognition applications. The spin-image provides a useful data representation for 3D point cloud data. In the spirit of recent work that shows ℓ1-sparseness to be a useful data compression metric, we propose to use Nonnegative Matrix Factorization (NMF) to help find features that capture the salient information resident in the spin-image representation. NMF is a technique for decomposing nonnegative multivariate data into its 'parts', resulting in a compressed and usually sparse representation. As a surrogate for measured 3D LADAR data, we generate 3D point clouds from computer-aided-design models of two land targets, and we generate spin-images at multiple support scales. We select the support scale that provides the highest separability between the spin-image stacks from the two land targets. We then apply NMF to the spin-images at this support scale, and seek elements corresponding to meaningful parts of the land vehicles (e.g., a tank turret or truck wheels), that in a joint sense should provide significant discriminative capability. We measure the separability in the sparse NMF subspace. For measuring separability, we use the Henze-Penrose measure of multivariate distributional divergence.

Paper Details

Date Published: 14 April 2008
PDF: 9 pages
Proc. SPIE 6967, Automatic Target Recognition XVIII, 696710 (14 April 2008); doi: 10.1117/12.776964
Show Author Affiliations
Andrew J. Patterson, Raytheon Missile Systems (United States)
Nitesh N. Shah, Raytheon Missile Systems (United States)
Donald E. Waagen, Raytheon Missile Systems (United States)


Published in SPIE Proceedings Vol. 6967:
Automatic Target Recognition XVIII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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