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

Maria-Albertina Castro; Yann Frauel; Eduardo Tepichin; Bahram Javidi

doi:10.1117/12.473130

28 May 2003 Pose estimation using linear or nonlinear composite correlation filters and a neural network

Maria-Albertina Castro, Yann Frauel, Eduardo Tepichin, Bahram Javidi

Proceedings Volume 5014, Image Processing: Algorithms and Systems II; (2003) https://doi.org/10.1117/12.473130
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States

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.

Citation Download Citation

Maria-Albertina Castro, Yann Frauel, Eduardo Tepichin, and Bahram Javidi "Pose estimation using linear or nonlinear composite correlation filters and a neural network", Proc. SPIE 5014, Image Processing: Algorithms and Systems II, (28 May 2003); https://doi.org/10.1117/12.473130

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