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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 42, Iss. 29 — Oct. 10, 2003
  • pp: 5882–5890

Pose estimation from a two-dimensional view by use of composite correlation filters and neural networks

Albertina Castro, Yann Frauel, Eduardo Tepichín, and Bahram Javidi  »View Author Affiliations


Applied Optics, Vol. 42, Issue 29, pp. 5882-5890 (2003)
http://dx.doi.org/10.1364/AO.42.005882


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Abstract

We present a technique to estimate the pose of a three-dimensional object from a two-dimensional view. We first compute the correlation between the unknown image and several synthetic-discriminant-function filters constructed with known views of the object. We consider both linear and nonlinear correlations. The filters are constructed in such a way that the obtained correlation values depend on the pose parameters. We show that this dependence is not perfectly linear, in particular for nonlinear correlation. Therefore we use a two-layer neural network to retrieve the pose parameters from the correlation values. We demonstrate the technique by simultaneously estimating the in-plane and out-of-plane orientations of an airplane within an 8-deg portion. We show that a nonlinear correlation is necessary to identify the object and also to estimate its pose. On the other hand, linear correlation is more accurate and more robust. A combination of linear and nonlinear correlations gives the best results.

© 2003 Optical Society of America

OCIS Codes
(100.5010) Image processing : Pattern recognition
(100.6740) Image processing : Synthetic discrimination functions
(200.4260) Optics in computing : Neural networks

History
Original Manuscript: January 30, 2003
Revised Manuscript: June 2, 2003
Published: October 10, 2003

Citation
Albertina Castro, Yann Frauel, Eduardo Tepichín, and Bahram Javidi, "Pose estimation from a two-dimensional view by use of composite correlation filters and neural networks," Appl. Opt. 42, 5882-5890 (2003)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-42-29-5882


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