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Proceedings Paper

Model-based 3D object pose estimation from linear image decomposition and direction of arrival analysis
Author(s): David Cyganski; Richard F. Vaz; Charles R. Wright
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Paper Abstract

This paper presents enhancements and new results related to a method for model-based object recognition which uses a single, comprehensive analytic object model representing the entirety of a suite of gray-scale views of the object. Object orientation and identity are directly established by this method from arbitrary views, even though these views are not related by any geometric image transformation. The approach is also applicable to multi-sensor real and complex data, such as radar and thermal signatures. The object model is comprised of a reduced reciprocal image set generated from a Fourier representation of an object image suite. The projection of an acquired image onto the reciprocal basis yields samples of a complex exponential, the phase of which reveals the pose parameters. Estimation of this phase for several degrees of freedom corresponds to the plane wave direction of arrival (DOA) problem; thus the pose parameters can be found using DOA solution techniques. Results are given which illustrate the performance of an implementation of this method using camera acquired images.

Paper Details

Date Published: 20 April 1993
PDF: 9 pages
Proc. SPIE 1827, Model-Based Vision, (20 April 1993); doi: 10.1117/12.143068
Show Author Affiliations
David Cyganski, Worcester Polytechnic Institute (United States)
Richard F. Vaz, Worcester Polytechnic Institute (United States)
Charles R. Wright, Worcester Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 1827:
Model-Based Vision
Hatem N. Nasr; Rodney M. Larson, Editor(s)

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