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2009 8th IEEE International Symposium on Mixed and Augmented Reality
Shape recognition and pose estimation for mobile augmented reality
Orlando, FL, USA
October 19-October 22
ISBN: 978-1-4244-5390-0
Nate Hagbi, The Visual Media Lab, Ben-Gurion University, Israel
Oriel Bergig, The Visual Media Lab, Ben-Gurion University, Israel
Jihad El-Sana, The Visual Media Lab, Ben-Gurion University, Israel
Mark Billinghurst, The HIT Lab NZ, University of Canterbury, New Zealand
In this paper we present Nestor, a system for real-time recognition and camera pose estimation from planar shapes. The system allows shapes that carry contextual meanings for humans to be used as Augmented Reality (AR) tracking fiducials. The user can teach the system new shapes at runtime by showing them to the camera. The learned shapes are then maintained by the system in a shape library. Nestor performs shape recognition by analyzing contour structures and generating projective invariant signatures from their concavities. The concavities are further used to extract features for pose estimation and tracking. Pose refinement is carried out by minimizing the reprojection error between sample points on each image contour and its library counterpart. Sample points are matched by evolving an active contour in real time. Our experiments show that the system provides stable and accurate registration, and runs at interactive frame rates on a Nokia N95 mobile phone.
Citation:
Nate Hagbi, Oriel Bergig, Jihad El-Sana, Mark Billinghurst, "Shape recognition and pose estimation for mobile augmented reality," ismar, pp.65-71, 2009 8th IEEE International Symposium on Mixed and Augmented Reality, 2009
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