Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
Online 6 DOF Augmented Reality Registration from Natural Features
ISMAR '02 Proceedings of the 1st International Symposium on Mixed and Augmented Reality
Marker Tracking and HMD Calibration for a Video-Based Augmented Reality Conferencing System
IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality
Markerless Augmented Reality with a Real-Time Affine Region Tracker
ISAR '01 Proceedings of the IEEE and ACM International Symposium on Augmented Reality (ISAR'01)
Two-Frame Wide Baseline Matching
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Stable Real-Time 3D Tracking Using Online and Offline Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene Modelling, Recognition and Tracking with Invariant Image Features
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Online Estimation of Trifocal Tensors for Augmenting Live Video
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Registration Using Natural Features for Augmented Reality Systems
IEEE Transactions on Visualization and Computer Graphics
Real-Time Markerless Tracking for Augmented Reality: The Virtual Visual Servoing Framework
IEEE Transactions on Visualization and Computer Graphics
Technical Section: A generalized registration method for augmented reality systems
Computers and Graphics
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This paper proposes a robust point transferring method for markerless AR applications. Using this method, any points specified at the initialization stage can be stably transferred during the augmentation process. These transferred points can be used for registration, annotation and video augmentation in markerless AR applications. This proposed point transferring method is based on a simple nonlinear optimization model. The proposed method has several advantages. Firstly, it is robust and stable as it remains effective when the camera is moved about quickly or when the scenes are largely occluded or filled with moving objects. Second, it is simple as the points that will be used for registration, annotation and video augmentation are only required to be specified in one image. Lastly, it is fast as the proposed simple optimization model can be solved quickly. Several experiments have been conducted to validate the performance of this proposed method.