Fully automated and stable registration for augmented reality applications
- 1 March 2004
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
We present a fully automated approach to camera registrationfor Augmented Reality systems. It relies on purelypassive vision techniques to solve the initialization and real-timetracking problems, given a rough CAD model of partsof the real scene. It does not require a controlled environment,for example placing markers. It handles arbitrarilycomplex models, occlusions, large camera displacementsand drastic aspect changes.This is made possible by two major contributions: Thefirst one is a fast recognition method that detects the knownpart of the scene, registers the camera with respect to it, andinitializes a real-time tracker, which is the second contribution.Our tracker eliminates drift and jitter by merging theinformation from preceding frames in a traditional recursivetracking fashion with that of a very limited number ofkey-frames created off-line. In the rare instances where itfails, for example because of large occlusion, it detects thefailure and reinvokes the initialization procedure.We present experimental results on several differentkinds of objects and scenes.Keywords
This publication has 19 references indexed in Scilit:
- Marker tracking and HMD calibration for a video-based augmented reality conferencing systemPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Real-time 100 object recognition systemPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Learning, positioning, and tracking visual appearancePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Adaptive tracking and model registration across distinct aspectsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Solution of the Simultaneous Pose and Correspondence Problem Using Gaussian Error ModelComputer Vision and Image Understanding, 1999
- Object recognition from local scale-invariant featuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1999
- Local grayvalue invariants for image retrievalPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1997
- Color indexingInternational Journal of Computer Vision, 1991
- Eigenfaces for RecognitionJournal of Cognitive Neuroscience, 1991
- Robust Regression and Outlier DetectionPublished by Wiley ,1987