Shape recognition and pose estimation for mobile augmented reality

Nate Hagbi, Oriel Bergig, Jihad El-Sana, Mark Billinghurst

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

Nestor is a real-time recognition and camera pose estimation system for planar shapes. The system allows shapes that carry contextual meanings for humans to be used as Augmented Reality (AR) tracking targets. The user can teach the system new shapes in real time. New shapes can be shown to the system frontally, or they can be automatically rectified according to previously learned shapes. Shapes can be automatically assigned virtual content by classification according to a shape class 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.

Original languageEnglish
Article number5620901
Pages (from-to)1369-1379
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Volume17
Issue number10
DOIs
StatePublished - 9 May 2011

Keywords

  • Multimedia information systems
  • and virtual realities
  • artificial
  • augmented
  • image processing and computer vision
  • scene analysis
  • tracking.

Fingerprint

Dive into the research topics of 'Shape recognition and pose estimation for mobile augmented reality'. Together they form a unique fingerprint.

Cite this