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Proceedings Paper

Pose estimation using linear or nonlinear composite correlation filters and a neural network
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Paper Abstract

Cameras provide only bi-dimensional views of three-dimensional objects. These views are projections that change depending on the spatial orientation or pose of the object. In this paper we propose a technique to estimate the pose of a 3D object knowing only a 2D picture of it. The proposed technique explores both the linear and the nonlinear composite correlation filters in a combination with a neural network. We present results in estimating two orientations: in-plane and out-of-plane rotations within an 8 degree square range.

Paper Details

Date Published: 28 May 2003
PDF: 9 pages
Proc. SPIE 5014, Image Processing: Algorithms and Systems II, (28 May 2003); doi: 10.1117/12.473130
Show Author Affiliations
Maria-Albertina Castro, Instituto Nacional de Astrofisica, Optica y Electronica (Mexico)
Yann Frauel, Univ. Nacional Autonoma de Mexico (Mexico)
Eduardo Tepichin, Instituto Nacional de Astrofisica, Optica y Electronica (Mexico)
Bahram Javidi, Univ. of Connecticut (United States)


Published in SPIE Proceedings Vol. 5014:
Image Processing: Algorithms and Systems II
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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