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DC optimization modeling for shape-based recognition
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Paper Abstract

This paper addresses several fundamental problems that have hindered the development of model-based recognition systems: (a) The feature-correspondence problem whose complexity grows exponentially with the number of image points versus model points, (b) The restriction of matching image data points to a point-based model (e.g., point based features), and (c) The local versus global minima issue associated with using an optimization model. Using a convex hull representation for the surfaces of an object, common in CAD models, allows generalizing the point-to-point matching problem to a point-to-surface matching problem. A discretization of the Euclidean transformation variables and use of the well known assignment model of Linear Programming renown leads to a multilinear programming problem. Using a logarithmic/exponential transformation employed in geometric programming this nonconvex optimization problem can be transformed into a difference of convex functions (DC) optimization problem which can be solved using a DC programming algorithm.

Paper Details

Date Published: 28 April 2009
PDF: 12 pages
Proc. SPIE 7337, Algorithms for Synthetic Aperture Radar Imagery XVI, 73370O (28 April 2009); doi: 10.1117/12.820293
Show Author Affiliations
Kirk Sturtz, Universal Mathematics (United States)
Gregory Arnold, Air Force Research Lab. (United States)
Matthew Ferrara, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 7337:
Algorithms for Synthetic Aperture Radar Imagery XVI
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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