Analysis of Head Pose Accuracy in Augmented Reality
IEEE Transactions on Visualization and Computer Graphics
Camera-Based ID Verification by Signature Tracking
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
ISMAR '02 Proceedings of the 1st International Symposium on Mixed and Augmented Reality
3D Live: Real Time Captured Content for Mixed Reality
ISMAR '02 Proceedings of the 1st International Symposium on Mixed and Augmented Reality
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Segmentation and recognition of multi-attribute motion sequences
Proceedings of the 12th annual ACM international conference on Multimedia
The AR Apprenticeship: Replication and Omnidirectional Viewing of Subtle Movements
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Mixed-Reality Simulation of Minimally Invasive Surgeries
IEEE MultiMedia
Collocated AAR: Augmenting After Action Review with Mixed Reality
ISMAR '08 Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
Eye-gaze driven surgical workflow segmentation
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Recovery of surgical workflow without explicit models
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
| Hi-index | 0.00 |
In our poster presentation at ISMAR'04 [11], we proposed the idea of an AR training solution including capture and 3D replays of subtle movements. The crucial part missing for realizing such a training system was an appropriate way of synchronizing trajectories of similar movements with varying speed in order to simultaneously visualize the motion of experts and trainees, and to study trainees' performances quantitatively. In this paper we review the research from different communities on synchronization problems of similar complexity. We give a detailed description of the two most applicable algorithms. We then present results using our AR based forceps delivery training system and therefore evaluate both methods for synchronization of experts' and trainees' 3D movements. We also introduce the first concepts of an online synchronization system allowing the trainee to follow movements of an expert and the experts to annotate 3D trajectories for initiation of actions such as display of timely information. A video demonstration provides an overview of the work and a visual idea of what users of the proposed system could observe through their video-see through HMD.