Artificial Intelligence - Special volume on computer vision
Real-time vision-based camera tracking for augmented reality applications
VRST '97 Proceedings of the ACM symposium on Virtual reality software and technology
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Calibration-Free Augmented Reality
IEEE Transactions on Visualization and Computer Graphics
Calibration-Free Augmented Reality in Perspective
IEEE Transactions on Visualization and Computer Graphics
Recent Advances in Augmented Reality
IEEE Computer Graphics and Applications
Hybrid Tracking for Outdoor Augmented Reality Applications
IEEE Computer Graphics and Applications
Presence: Teleoperators and Virtual Environments
VR '99 Proceedings of the IEEE Virtual Reality
Hybrid Inertial and Vision Tracking for Augmented Reality Registration
VR '99 Proceedings of the IEEE Virtual Reality
Marker-less Tracking for AR: A Learning-Based Approach
ISMAR '02 Proceedings of the 1st International Symposium on Mixed and Augmented Reality
A Non-Iterative Greedy Algorithm for Multi-frame Point Correspondence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A real-time tracker for markerless augmented reality
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Point Matching under Large Image Deformations and Illumination Changes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Hybrid Tracking System for Outdoor Augmented Reality
VR '04 Proceedings of the IEEE Virtual Reality 2004
Registration Based on Projective Reconstruction Technique for Augmented Reality Systems
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Multimedia
Registration based on online estimation of trifocal tensors using point and line correspondences
ICVR'07 Proceedings of the 2nd international conference on Virtual reality
Markerless augmented reality for robotic helicoptor applications
RobVis'08 Proceedings of the 2nd international conference on Robot vision
Local structure-based region-of-interest retrieval in brain MR images
IEEE Transactions on Information Technology in Biomedicine
A multi-regional computation scheme in an AR-assisted in situ CNC simulation environment
Computer-Aided Design
Markerless augmented reality using a robust point transferring method
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
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Registration is one of the most difficult problems in augmented reality (AR) systems. In this paper, a simple registration method using natural features based on the projective reconstruction technique is proposed. This method consists of two steps: embedding and rendering. Embedding involves specifying four points to build the world coordinate system on which a virtual object will be superimposed. In rendering, the Kanade-Lucas-Tomasi (KLT) feature tracker is used to track the natural feature correspondences in the live video. The natural features that have been tracked are used to estimate the corresponding projective matrix in the image sequence. Next, the projective reconstruction technique is used to transfer the four specified points to compute the registration matrix for augmentation. This paper also proposes a robust method for estimating the projective matrix, where the natural features that have been tracked are normalized (translation and scaling) and used as the input data. The estimated projective matrix will be used as an initial estimate for a nonlinear optimization method that minimizes the actual residual errors based on the Levenberg-Marquardt (LM) minimization method, thus making the results more robust and stable. The proposed registration method has three major advantages: 1)It is simple, as no predefined fiducials or markers are used for registration for either indoor and outdoor AR applications. 2) It is robust, because it remains effective as long as at least six natural features are tracked during the entire augmentation, and the existence of the corresponding projective matrices in the live video is guaranteed. Meanwhile, the robust method to estimate the projective matrix can obtain stable results even when there are some outliers during the tracking process. 3) Virtual objects can still be superimposed on the specified areas, even if some parts of the areas are occluded during the entire process. Some indoor and outdoor experiments have been conducted to validate the performance of this proposed method.