Artificial Intelligence - Special volume on computer vision
Self-Calibration of a Moving Camera from PointCorrespondences and Fundamental Matrices
International Journal of Computer Vision
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
Augmented Reality Camera Tracking with Homographies
IEEE Computer Graphics and Applications
Projective Structure from Uncalibrated Images: Structure From Motion and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A Hybrid Registration Method for Outdoor Augmented Reality
ISAR '01 Proceedings of the IEEE and ACM International Symposium on Augmented Reality (ISAR'01)
Technical Section: A generalized registration method for augmented reality systems
Computers and Graphics
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In AR systems, registration is one of the most difficult problems currently limiting its applications. In this paper, we proposed a simple registration method using projective reconstruction. This method includes two steps: embedding and tracking. Embedding involves specifying four points to build the world coordinate system on which a virtual object will be superimposed. In tracking, a projective reconstruction technique in computer vision is used to track these specified four points to compute the modelview transformation for augmentation. This method is simple as only four points need to be specified at the embedding stage, and the virtual object can then be easily augmented onto a real video sequence. In addition, it can be extended to a general scenario using a generic projective matrix. The proposed method has three advantages: (1) It is fast because the linear least square method can be used to estimate the related matrix in the algorithm and it is not necessary to calculate the fundamental matrix in the extended case; (2) A virtual object can still be superimposed on a related area even if some parts of the specified area are occluded during the whole process; (3) This method is robust because it remains effective even when not all the reference points are detected during the whole process (in the rendering process), if at least six pairs of related reference point correspondences can be found. Several projective matrices obtained from the authors' previous work, which is unrelated with the present AR system, have been tested on this extended registration method. Experiments showed that these projective matrices can also be utilized for tracking the specified points.