Registration based on projective reconstruction technique for augmented reality systems.
In AR systems, registration is one of the most difficult problems currently limiting their application. In this paper, we propose a simple registration method using projective reconstruction. This method consists of 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 is used to track these four specified points to compute the model view 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 scene from a video sequence. In addition, it can be extended to a scenario using the projective matrix that has been obtained from previous registration results using the same AR system. 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, as long as at least six pairs of related reference points correspondences can be found. Some experiments have been conducted to validate the performance of the proposed method.
Yuan
M L
ML
Singapore-MIT Alliance, National University of Singapore, 9 Engineering Drive 1, Singapore 117576. smayml@nus.edu.sg
Ong
S K
SK
Nee
A Y C
AY
D000465:Algorithms; D001185:Artificial Intelligence; D003196:Computer Graphics; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique; D014584:User-Computer Interface