Tracking based structure and motion recovery for augmented video productions
VRST '01 Proceedings of the ACM symposium on Virtual reality software and technology
Online 6 DOF Augmented Reality Registration from Natural Features
ISMAR '02 Proceedings of the 1st International Symposium on Mixed and Augmented Reality
An Adaptive Estimator for Registration in Augmented Reality
IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality
Fully Automated and Stable Registration for Augmented Reality Applications
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Robust Pose Estimation from a Planar Target
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markerless Augmented Reality Using Image Mosaics
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Vision-Based Markerless Gaming Interface
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Vision-Based Markerless Gaming Interface
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
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The ability to augment a video stream with consistent virtual contents is an attractive Computer Vision application. The first Augmented Reality (AR) proposals required the scene to be endowed with special markers. Recently, thanks to the developments in the field of natural invariant local features, similar results have been achieved in a markerless scenario. The computer vision community is now equipped with a set of relatively standard techniques to solve the underlying markerless camera pose estimation problem, at least for planar textured reference objects. The majority of proposals, however, does not exploit temporal consistency across frames in order to reduce some disturbing effects of per-frame estimation, namely visualization of short spurious estimations and jitter. We proposes a new method based on Support Vector Regression to mitigate these undesired effects while preserving the ability to work in real-time. Our proposal can be used as a post processing step independent of the chosen pose estimation method, thus providing an effective and easily integrable building block for AR applications.