International Journal of Computer Vision - 1998 Marr Prize
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Calibration-Free Augmented Reality in Perspective
IEEE Transactions on Visualization and Computer Graphics
Recursive Estimation of Motion, Structure, and Focal Length
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure from Motion Causally Integrated Over Time
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Camera Recovery for Closed or Open Image Sequences
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Balanced Recovery of 3D Structure and Camera Motion from Uncalibrated Image Sequences
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Frame Decimation for Structure and Motion
SMILE '00 Revised Papers from Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
In defence of the 8-point algorithm
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Accurate Camera Calibration for Off-line, Video-Based Augmented Reality
ISMAR '02 Proceedings of the 1st International Symposium on Mixed and Augmented Reality
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Visual Modeling with a Hand-Held Camera
International Journal of Computer Vision
3D point-of-regard, position and head orientation from a portable monocular video-based eye tracker
Proceedings of the 2008 symposium on Eye tracking research & applications
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In this paper, we propose a new recursive framework for camera resectioning and apply it to off-line video-based augmented reality. Our method is based on an unscented particle filter and an independent Metropolis-Hastings chain, which deal with nonlinear dynamic systems without local linearization, and lead to more accurate results than other nonlinear filters. The proposed method has some desirable properties for camera resectioning: Since it does not rely on erroneous linear solutions, initialization problems do not occur, in contrast to the previous resectioning methods. Jittering error can be reduced by considering consistency and coherency between adjacent frames in our recursive framework. Our method is fairly accurate comparable to nonlinear optimization methods, which in general have higher levels of computation and complexity. As a result, the proposed algorithm outperforms the standard camera resectioning algorithm. We verify the effectiveness of our method through several experiments using synthetic and real image sequences comparing the estimation performance with other linear and nonlinear methods.