Radon space and Adaboost for Pose Estimation

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 associated with [AdaBoost] learners that capture the wide image feature spectrum of a given 3D line. Such a framework is used through inference for pose estimation. Given a new image, we extract features which are consistent with the ones learnt, and then we associate such features with a number of lines in the 3D plane that are pruned through the use of geometric constraints. Once correspondence between lines has been established, pose estimation is done in a straightforward fashion. Encouraging experimental results based on a real case demonstrate the potentials of our method.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Patrick Etyngier, Nikos Paragios, Renaud Keriven, Yakup Genc, Jean-Yves Audibert
• Keywords: Camera Pose Estimation, Computer Vision, Exact Camera Position, ICPR 2006, Image Feature Spectrum