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Radon space and Adaboost for Pose Estimation Patrick Etyngier1

Summary: Radon space and Adaboost for Pose Estimation
Patrick Etyngier1
Nikos Paragios2
Renaud Keriven1
Yakup Genc3
Jean-Yves Audibert1
CERTIS Laboratory 2
MAS Laboratory 3
Siemens Corporate Research
Ecole des Ponts, Paris, France Ecole Centrale Paris, France Princeton NJ, USA
etyngier@certis.enpc.fr nikos.paragios@ecp.fr yakup.genc@siemens.com
In this paper, we present a new approach to camera pose
estimation from single shot images in known environment.
Such a method comprises two stages, a learning step and
an inference stage where given a new image we recover the
exact camera position. Lines that are recovered in the radon
space consist of our feature space. Such features are associ-
ated with [AdaBoost] learners that capture the wide image


Source: Audibert, Jean-Yves - Département d'Informatique, École Normale Supérieure
Paragios, Nikos - Center for Visual Computing, Ecole Centrale de Paris
Paragios, Nikos - Centre d'Enseignement et de Recherche en Technologies de l'Information et Systèmes, Ecole Nationale des Ponts et Chaussées


Collections: Computer Technologies and Information Sciences