DC optimization modeling for shape-based recognition

Kirk Sturtz; Gregory Arnold; Matthew Ferrara

doi:10.1117/12.820293

28 April 2009 DC optimization modeling for shape-based recognition

Kirk Sturtz, Gregory Arnold, Matthew Ferrara

Proceedings Volume 7337, Algorithms for Synthetic Aperture Radar Imagery XVI; 73370O (2009) https://doi.org/10.1117/12.820293
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Kirk Sturtz, Gregory Arnold, and Matthew Ferrara "DC optimization modeling for shape-based recognition", Proc. SPIE 7337, Algorithms for Synthetic Aperture Radar Imagery XVI, 73370O (28 April 2009); https://doi.org/10.1117/12.820293

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