Pose estimation from a two-dimensional view by use of composite correlation filters and neural networks |
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Authors: | Castro Albertina Frauel Yann Tepichín Eduardo Javidi Bahram |
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Affiliation: | Instituto Nacional de Astrofísica, Optica y Electrónica, Apdo. Postal 51, Puebla, Puebla, 72000, México. betina@inaoep.mx |
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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. |
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